Home •Blog
TTMS Blog
TTMS experts about the IT world, the latest technologies and the solutions we implement.
Sort by topics
How to Create Business Apps – 2025 Guide
Creating a mobile app for business is no longer just a nice-to-have. It’s become essential. As digital transformation gains momentum across industries, companies that embrace mobile technologies are ahead of the competition. Whether you want to streamline your team’s workflow or better connect with your customers, learning to build a business app requires strategic thinking, technical expertise, and careful implementation. 1. Why your business needs a mobile app, current trends in the mobile application market The world of mobile apps continues to explode with growth. The global mobile app market reached $252.9 billion in 2023 and is expected to reach $626.4 billion by 2030. This massive growth is fundamentally changing the way businesses connect with customers and conduct business. Mobile devices dominate digital interactions today. Companies that utilize mobile apps gain greater brand visibility, stronger customer relationships, and a real competitive advantage. Interestingly, no-code and low-code platforms have made app development accessible to companies of all sizes. Industry experts predict that by 2025, as many as 70% of new projects will be based on these solutions. App development leaders also emphasize that AI-based predictive analytics are becoming standard in business applications. It’s no longer the exclusive domain of tech giants. This allows companies to deliver highly personalized user experiences, offering recommendations and interfaces that significantly increase engagement and keep users coming back. Another important trend is Progressive Web Apps. They combine the accessibility of websites with the functionality of native apps, a particularly clever solution. This hybrid approach allows companies to reach broader audiences while still providing users with the user experience of apps. On-demand applications are also an extremely strong growth category, with users spending almost $58 billion annually in this sector. 2. Types of business apps you can create Understanding how to build a business app begins with understanding the different types available. Customer-facing apps include e-commerce platforms, appointment booking systems, delivery tracking, and feedback tools. These apps have a direct impact on revenue and customer satisfaction. Internal applications focus on streamlining processes, such as team management platforms, workflow automation tools, and communication systems. There are also industry-specific solutions that address specific needs, such as restaurant ordering systems, real estate listing platforms, medical forms, and event registration tools. Modern application development is flexible enough to create solutions tailored to your processes or niche markets. A simple information application can evolve into a complex platform with payment processing, inventory management, and extensive reporting. 3. Planning a business application strategy 3.1 Defining the purpose and assumptions of the application Learning how to create an app idea begins with a clear understanding of its purpose. Your app should solve specific problems or provide real value to users. Setting measurable goals provides a roadmap for feature development and benchmarks for tracking success. Opar is a good example. This company successfully launched a social app by focusing on user-centric design and advanced matching algorithms that connect people based on location and interests. Ensure your app’s goals align with your broader business strategy. This ensures your app supports your business’s growth, rather than operating in isolation. Ask yourself: is your top priority customer engagement, revenue generation, process improvement, or brand enhancement? A clear answer will shape every decision you make during the development process. 3.2 Target group identification You need to thoroughly understand the demographics, behaviors, and pain points of your audience. This is the foundation of effective app development. Research reveals who will benefit most from your solution and helps prioritize features. A good example is the fitness app of a major sportswear brand. Through data analysis and user research, they discovered that easy navigation and personalized content were key. The result? A 40% increase in user retention and a 60% increase in active engagement. Creating detailed user profiles supports marketing and communication strategies. This research step protects against costly mistakes and ensures your app meets the needs of the right audience. Be sure to include both primary and secondary users, as different types of people may use your app differently. 4. Conducting market research and competitive analysis In-depth market research validates your app idea and demonstrates real demand. Competitive analysis reveals industry standards, popular features, and opportunities for differentiation. Understanding existing solutions allows you to leverage best practices and better understand user expectations in your market segment. Analyzing failed apps provides valuable insights into common mistakes and poor decisions. This knowledge helps you make smarter development choices and avoid repeating the mistakes of others. Market research also reveals effective pricing strategies, monetization models, and user acquisition methods in your industry. 5. Creating user personas and usage scenarios Developing detailed user personas helps you anticipate needs and design features that actually serve them. These extensive profiles represent your ideal audience, taking into account their goals, frustrations, and behavioral patterns. Usage scenario mapping clarifies how different types of users will use your app in real-world situations. This process ensures the application remains intuitive and addresses the problems users actually face. Usage scenarios provide guidance in developing functional requirements and designing user journeys, creating a roadmap to seamless experiences. Well-defined personas and scenarios provide a reference point at every stage of development, keeping the team focused on real user needs. 6. Choosing the right approach to app development 6.1 Native app development 6.1.1 Native iOS App Development Native iOS apps are built using Apple’s development tools and programming languages like Swift and Objective-C. This approach ensures superior performance and seamless integration with the iOS ecosystem. However, apps must meet Apple’s stringent guidelines and undergo the App Store’s review process. Native iOS development provides access to the latest Apple features and maintains consistency with the platform’s design standards. However, it requires specialized knowledge of the operating system and allows for the development of apps exclusively for Apple devices. 6.1.2 Native Android app development Native Android apps are developed in Java or Kotlin within Android Studio. This approach leverages the diversity of Android devices and their customization capabilities. A more flexible distribution model allows apps to be made available not only through the Google Play Store but also through other channels. Native Android development works well with a variety of Android hardware and provides deep integration with Google services. Similar to iOS, it requires platform-specific knowledge and allows for the development of single-system solutions. 6.2 Advantages and disadvantages of native applications Native development provides superior performance, full access to device features, and a refined user experience that fits naturally into the platform. Such apps typically load faster, run more smoothly, and integrate seamlessly with device features like the camera, GPS, and sensors. The main disadvantages are longer development time and higher costs, as a separate application must be created for each platform. Native development also requires specialized knowledge of each operating system, which can mean doubling resources and extending the project timeline. 7. Progressive web applications (PWA) 7.1 When to choose PWA for business PWAs are ideal for situations where companies want broad availability without the need for publishing to app stores. This approach is ideal for businesses that require rapid updates, SEO benefits, and compatibility with various devices. PWAs are a perfect fit for content-rich apps or services that require frequent updates. PWAs are a good choice when your users value convenience over advanced functionality. They’re a great solution for companies that want to test market demand before investing in full native development, or for those that support users across devices and platforms. 7.2 Benefits of PWA development PWAs provide a native app-like experience through a web browser while maintaining web accessibility. They work offline, update automatically, and eliminate app store fees and approval processes. Users can use PWAs immediately without downloading them, lowering the barrier to entry. Such solutions are built on a single codebase, reducing maintenance complexity. PWAs remain visible in search engines, offering SEO advantages that traditional apps lack. This is a particularly cost-effective solution for companies that prioritize reach over advanced hardware integration. 8. Creating cross-platform applications 8.1 React Native and Flutter options Cross-platform frameworks like React Native and Flutter enable the creation of iOS and Android apps from a single codebase. CTOs and digital strategy leaders regularly recommend these solutions for their code reuse, fast and cost-effective development cycles, and consistent user experiences across platforms. This approach reduces development time and costs compared to separate native development. React Native uses JavaScript, a language familiar to many developers, while Flutter uses Dart, enabling the creation of highly flexible interfaces. Both frameworks enjoy strong community support and regular updates from major tech companies. 8.2 Hybrid solutions Hybrid application development combines web technologies with native containers, allowing for rapid application deployment across platforms. This approach is effective for moderately complex applications that don’t require full native performance. Hybrid solutions often enable faster time-to-market, which is crucial for companies prioritizing time-to-market over maximum performance. Modern hybrid frameworks have significantly reduced the performance gap compared to native applications. They are particularly suitable for content-driven applications or business tools where user interface consistency is more important than intensive computing capabilities. 9. No-Code and Low-Code Platforms 9.1 The Best No-Code App Builders for Business No-code platforms offer application development using drag-and-drop interfaces and pre-built templates. Industry experts emphasize that low-code/no-code solutions enable even those without programming experience to create applications for rapid prototyping and increased business agility. These tools allow companies to build functional applications without any programming knowledge, making them ideal for prototypes, MVPs, and simple business applications. Popular no-code solutions offer industry-specific templates, integrated databases, and publishing features. They are especially valuable for small businesses or departments that want to test concepts before committing to a dedicated solution. Many platforms also offer analytics, user management, and basic e-commerce features. 9.2 Limitations and Considerations No-code and low-code platforms have limitations in terms of customization, scalability, and access to advanced features. They are best suited for simple applications or as a starting point before moving on to dedicated development. Complex business logic or unique project requirements may exceed the capabilities of these tools. When choosing no-code solutions, consider long-term development plans. While they allow for a quick start and lower initial costs, you may eventually need dedicated development as your requirements grow. Check the platform provider’s stability and data export options to avoid future migration issues. 10. Power Apps in practice Power Apps is not just a platform for rapid application development, but a way to truly transform organizational operations. The following examples demonstrate how companies are using TTMS solutions based on Power Apps to automate processes, save time, and improve team efficiency. 10.1 Leave Manager – quick leave reporting and approval In many organizations, the leave request process is inefficient and opaque. Leave Manager automates the entire process—from request submission to approval. Employees can submit leave requests in just a few clicks, and managers gain real-time visibility into team availability. The application ensures complete transparency, shortens response times, and eliminates errors resulting from manual processing. 10.2 Smart Office Supply – Shopping App Daily office operations often suffer from chaotic reporting of faults or material shortages. Smart Office Supply centralizes this process, enabling quick reporting of needs—from missing coffee to equipment failures. The application integrates with Microsoft 365, sends email and Teams notifications to the appropriate people, and all requests are archived in one place. The result? Time savings, greater transparency, and a modern office image. 10.3 Benefit Manager – digital management of Social Benefits Fund benefits Paper applications, emails, and manual filing are a thing of the past. Benefit Manager completely digitizes the Company Social Benefits Fund (ZFŚS) process. Employees submit applications online, and the system automatically routes them to the appropriate person. Integration with Microsoft 365 makes the process fully GDPR-compliant, transparent, and measurable. HR saves time, and employees gain a convenient digital experience. 10.4 Device Manager – company hardware management Device Manager streamlines the management of IT assets—computers, phones, and corporate devices. Administrators can assign devices to users, track their status and service history, and log repairs and maintenance. The application automates hardware replacement and failure reporting processes, minimizing the risk of device loss and increasing control over IT resources. 10.5 Safety Check – workplace safety In factories and production plants, rapid response to threats is crucial. Safety Check is a Power App for occupational health and safety inspectors that enables immediate risk reporting using photos and location. Users can track the progress of corrective actions, generate reports, and confirm hazard removal. The solution increases safety, supports regulatory compliance, and improves communication within production teams. Each of the above applications demonstrates that Power Apps is a tool that allows you to quickly translate business needs into working solutions. Combining a simple interface with Power Automate and Power BI integration, the platform supports digital transformation in practice – from the office to the production floor. 11. Step-by-step application development process 11.1 Step 1: Wireframe and Prototyping Wireframes establish the structural foundation of an app, defining key navigation and user flow before visual design begins. They can be compared to architectural plans that define the layout of rooms before interior design. This stage focuses on functionality and optimizing the user journey, rather than aesthetics. Prototyping brings wireframes to life, creating interactive models that showcase user experiences. Early prototypes reveal usability issues and allow you to gather stakeholder feedback before making larger development investments. Iterative refinement during the prototyping phase saves significant time and resources in later development phases. 11.2 Step 2: UI/UX Design for Business Applications User interface and experience design transforms functional wireframes into engaging, intuitive applications. Effective business app design balances simplicity with functionality while maintaining brand consistency. Design choices should ensure easy navigation, fast loading, and enjoyable interactions that encourage regular use. Digital transformation experts emphasize that AR integration delivers high ROI in sectors like retail, education, and healthcare, enabling interactive, real-world experiences. For example, IKEA, which uses furniture visualization to reduce returns and increase conversions, is a key example. When designing business applications, consider the user context. Internal tools may prioritize efficiency and data density, while customer-facing applications prioritize visual appeal and ease of use. Considering accessibility requirements ensures that the application will be usable by people with diverse needs and abilities. 11.3 Step 3: Selecting the technology The technology stack determines an application’s capabilities, performance, and future scalability. Enterprise IT strategists consistently recommend cloud infrastructure because it supports scalability and innovation, enables easy global deployment, flexible scaling, and a usage-based cost model. The technology choice influences development speed, maintenance requirements, and specialist availability. Factors such as team expertise, project timeline, budget constraints, and scalability needs must be considered. Popular technology stacks offer extensive documentation and integrations with external solutions, while newer technologies can offer performance advantages, although they often have smaller support communities. 11.4 Step 4: Backend and Database Configuration Backend systems are responsible for data storage, user authentication, business logic, and API connections that drive application functionality. Much like a restaurant kitchen, the backend remains invisible to users, yet it determines the quality and reliability of the service. A robust backend architecture ensures secure and scalable performance under variable load conditions. Database selection impacts data retrieval speed, storage costs, and scalability. Data types, query patterns, and growth projections should be considered when deciding between relational and NoSQL databases. Cloud solutions often offer better scalability and lower maintenance costs than self-hosted options. 11.5 Step 5: Frontend and User Interface The front-end transforms design mockups into interactive user interfaces that interface with back-end systems. This stage requires careful attention to responsive design to ensure consistent experiences across screens and devices. Performance optimization is crucial because front-end code directly impacts users’ perception of the application’s speed and reliability. Integration between frontend and backend must be seamless to ensure a seamless user experience. API connections, data synchronization, and error handling require thorough testing to avoid user frustration and data inconsistency. 11.6 Step 6: Integrating APIs and External Services API integrations expand an application’s capabilities by connecting it to external services such as payment systems, maps, social media platforms, and business tools. Such solutions accelerate development and provide professional functionality that would be costly to develop internally. When selecting external services, ensure APIs are reliable and secure. It’s important to prepare contingency plans for critical integrations and monitor service availability to maintain application stability. Documenting API dependencies facilitates future maintenance and updates. 11.7 Step 7: Testing and quality control Comprehensive testing helps detect bugs, usability issues, and performance bottlenecks before users encounter them. Testing should encompass functionality across devices, operating system versions, and network conditions. Security testing is particularly important for business applications handling sensitive data or financial transactions. Automated testing tools can streamline iterative testing, while manual testing can catch subtle usability issues that might escape automation. Beta testing with real users provides valuable feedback on actual app usage patterns and audience preferences. 12. Key features of business applications 12.1 Basic functional requirements The most important features must be directly linked to the application’s primary purpose and user needs. Prioritizing core functionality ensures immediate value while avoiding unnecessary complexity that could discourage users or increase development costs. Core features provide the foundation upon which subsequent application elements can be built. Clearly defining priorities helps manage project scope and budget constraints. It’s important to consider which features are absolutely essential for launching the app and which can be added in later updates. This approach allows you to get your app to market faster while maintaining a focus on user value. 12.2 User authentication and security Secure login protects user data and builds trust in the business application. Implementation should balance security requirements with ease of use, avoiding overly complex processes that could discourage use. Multi-factor authentication, strong password requirements, and session management are the foundations of security. Regular security audits and updates protect against new threats and support compliance with industry regulations. Business applications often process sensitive data, so security should be a priority, impacting both user adoption and regulatory compliance. 12.3 Push notifications and messaging systems Well-thought-out push notifications engage users by providing them with timely, relevant information about new products, offers, and important reminders. An effective notification strategy should deliver value without being intrusive or overwhelming. Users should be able to manage their preferences themselves to maintain a positive experience. In-app messaging features can support customer service, user interactions, or internal communication between business teams. Such solutions extend the value of the app by reducing the need for external tools and keeping all interactions within a single platform. 12.4 Analytics and reporting tools Built-in analytics provide insights into user behavior, feature usage, and app key performance indicators. This data supports business decisions, guides feature development, and allows you to measure return on investment. Analytics helps pinpoint features that are performing best and areas for improvement. Reporting tools should present data in formats that enable quick decision-making. It’s important to determine which metrics are most relevant to your business goals and design reports to clearly highlight key KPIs. 12.5 Payment integration Secure payment processing is essential for business applications that process transactions. Integration with trusted payment providers builds user trust and supports compliance with financial regulations. Providing a variety of payment methods addresses diverse user preferences and can increase conversion rates. The reliability of your payment system directly impacts revenue and customer trust. Choose providers with a proven track record of security, good customer service, and transparent costs. Thoroughly test your payment processes in various scenarios and across multiple devices. 12.6 Offline functionality The ability to use an application offline increases its reliability and user satisfaction, especially in environments with limited network access. Key features should remain accessible without an internet connection, and data synchronization should occur automatically when an internet connection is restored. This functionality can distinguish your application from the competition. Determine which features are most important offline and design appropriate data caching strategies. Users should be clearly informed when they are offline and how this impacts app performance. 12.7 Customer support features Integrated support options like chat, FAQs, and contact forms improve user satisfaction and reduce support costs. Easy access to support builds trust and allows for quick resolution of issues before they escalate into negative reviews or app abandonment. Self-service options often allow users to quickly resolve basic issues while reducing the burden on support teams. Help functions should be easily accessible and offer clear paths to resolution for different types of users. 13. Budget and timeline for app development 13.1 Cost breakdown by development method App development costs vary significantly depending on the chosen approach, level of complexity, and required features. Recent industry data shows that business mobile app development costs range from $40,000 to over $400,000, depending on complexity. Simple apps typically cost between $40,000 and $100,000, medium-complexity apps between $100,000 and $200,000, and advanced apps can reach $200,000–$400,000 or more. Cross-platform development using frameworks like Flutter or React Native can reduce costs compared to building standalone native apps. Development rates average between $25 and $49 per hour, varying by region, developer experience, and platform complexity. No-code platforms offer the lowest upfront costs but can generate higher long-term expenses due to monthly subscriptions and limited customization options. For example, a comprehensive marketplace app with reservations, payments, and reviews required around $300,000 or more for full platform development, while apps with IoT integration typically start at $60,000, depending on the complexity of the devices supported. 13.2 Hidden costs to consider Beyond initial development costs, ongoing costs must be considered, which significantly impact the budget. Annual maintenance costs average around 20% of the initial application development cost, including updates, bug fixes, and improvements. Marketing is a significant investment, with annual costs ranging from 50% to 100% of the initial development budget. Additional expenses include integrations with external services ($5,000–$20,000 per year), backend infrastructure ($20,000–$100,000), app store fees, server hosting, and ongoing support resources. It’s worth planning these recurring costs in advance to avoid budget surprises that could impact app quality or business stability. 13.3 Estimated timeline for different application types Application development time varies depending on the level of complexity and the approach taken. Simple applications require 3 to 6 months of work, medium-complexity applications 6 to 9 months, and complex enterprise-class solutions can take anywhere from 9 to 18 months or longer. Real-world examples demonstrate how these timelines play out: the social app Opar was developed in about 4–6 months, while the comprehensive marketplace platform required over 9 months. It’s also worth factoring in the time it takes for apps to be approved in marketplaces, which can take several weeks and require rework. 13.4 Financing options for app development Funding for an app project can come from a variety of sources, such as self-funding, crowdfunding, angel investors, or venture capital funds. Each option comes with its own set of requirements, timelines, and implications for business control and future strategic decisions. Preparing a compelling investment presentation with a clearly defined value proposition, market analysis, and financial forecasts increases your chances of securing financing. It’s also worth considering how different funding sources align with your business goals and growth plans before making a commitment. 14. Business application testing 14.1 User Acceptance Testing (UAT) User acceptance testing (UAT) confirms that an application meets business requirements and user expectations before its public release. This is a crucial step in which real users perform common tasks to identify usability issues or missing features. UAT feedback often reveals discrepancies between developer assumptions and actual user needs. The success of a major sportswear brand’s fitness app demonstrates the importance of comprehensive user research—surveys and focus groups—which indicated that easy navigation and personalized content are key. The UAT phase should be well-planned, with clearly defined test scenarios, success criteria, and feedback collection methods. 14.2 Performance and load testing Performance tests verify the stability, speed, and responsiveness of an application under various usage conditions. Load tests simulate periods of peak traffic to identify potential bottlenecks or system failures. These tests ensure the application runs smoothly even under heavy traffic, preventing crashes that undermine user confidence. Testing should span devices, network conditions, and operating system versions to ensure consistent performance. In the fitness app example, performance optimization resulted in a 25% drop in bounce rate, demonstrating the real-world impact of thorough testing on business outcomes. 14.3 Safety testing and regulatory compliance Security testing identifies vulnerabilities that could threaten user data or business operations. This process is crucial for applications processing sensitive data, financial transactions, or regulated information. Regular security audits help maintain protection against new threats. Compliance requirements vary by industry and location, impacting aspects such as data storage and user consent processes. It’s important to understand applicable regulations early in the planning process to avoid costly rework or legal issues after the app’s launch. 14.4 Beta testing with real users Beta testing programs allow select users to use an app before its official release, allowing them to gather valuable feedback on functionality, usability, and appeal. Beta testers often uncover edge cases and unusual usage patterns that may have been missed during internal testing, leading to a more polished final product. Recruit beta testers who represent your target audience and provide them with clear channels for feedback. It’s important to balance the length of your beta testing with your launch schedule to ensure you have enough time to fix key bugs without losing development momentum. 15. Application maintenance and updating 15.1 Regular updates and feature improvements Continuous updates allow for bug fixes, performance improvements, and new features that keep users engaged. A well-known sportswear brand’s fitness app achieved impressive results thanks to strategic updates, increasing downloads by 50% and referral traffic by 70% after performance optimizations and new features. It’s important to plan your update schedule to balance new feature development with stability improvements. Changes should be clearly communicated to users, highlighting the benefits and improvements they will experience after the update. The frequency of new releases should align with user expectations and competitive market pressures. 15.2 Integration of user feedback Actively collecting and analyzing user feedback helps set development priorities and demonstrates a commitment to customer satisfaction. Feedback channels should be easily accessible and encourage honest sharing of experiences and suggestions for improvement. It’s worth developing a systematic process for reviewing, categorizing, and prioritizing feedback. While not all suggestions can be implemented, simply acknowledging them and explaining the decisions made builds brand loyalty and trust. 15.3 Performance monitoring and data analysis Continuous performance monitoring allows you to track usage patterns, identify technical issues, and measure key business success metrics. Analytics support fact-based decisions about feature development, user experience optimization, and business strategy adjustments. Monitor both technical performance indicators and business KPIs to understand how application performance impacts business results. It’s also important to set up alerts for critical issues that require immediate attention to maintain high user satisfaction. 15.4 Long-term application development strategy Planning for future development ensures that the application can adapt to changing business needs, technological advancements, and market conditions. An evolution strategy should consider scalability requirements, new platform capabilities, and changes in the competitive landscape. Create roadmaps that balance innovation and stability—so that new features enhance the user experience, not complicate it. Regular strategy reviews allow you to adjust your plans based on market feedback and business performance data. 16. The most common traps and how to avoid them 16.1 Technical challenges and how to solve them Technical issues such as platform fragmentation, complex integrations, or limited scalability can disrupt application development or cause long-term operational challenges. Proactive planning, proper technology stack selection, and comprehensive testing significantly mitigate these risks. Complex, feature-rich, or highly secure enterprise applications generate the highest costs and longest timelines due to requirements for a dedicated backend, regulatory compliance (e.g., HIPAA, GDPR), and advanced integrations. Partnering with experienced developers or partners specializing in these solutions, such as TTMS, helps overcome these challenges with expertise in AI implementation, system integration, and process automation. 16.2 User Experience (UX) Errors Poor design, unintuitive navigation, or slow app performance can discourage users, regardless of its functionality. Prioritizing intuitive interfaces, responsive design, and fast loading significantly improves user retention and satisfaction. A case study of a fitness app shows that improving user experience can significantly increase engagement levels. Regular usability testing during development helps detect user experience issues before they impact real-world users. Simple, clear design solutions often prove more effective than complex interfaces that try to do too much at once. 16.3 Security and compliance issues Inadequate security measures can lead to data leaks, legal consequences, and lasting damage to a company’s reputation. Implementing best security practices, conducting regular audits, and monitoring regulatory changes are key investments in business protection. Security issues should be considered at every stage of application development, not treated as an afterthought. The cost of properly implementing security measures is small compared to the potential losses resulting from their absence. 16.4 Budget overruns and schedule delays Underestimating project complexity, scope creep, and hidden costs are common causes of application implementation problems. Realistic budget planning with a financial reserve, a clearly defined project scope, and monitoring progress based on milestones help maintain implementation control. It’s also worth remembering that application maintenance can cost from 20% to as much as 100% of the initial project cost annually—incorporating this into the budget prevents financial surprises. Regular project reviews enable early detection of potential issues and course corrections before they become serious. Good communication between all stakeholders helps manage expectations and prevent misunderstandings that could lead to costly changes. 17. Summary Building effective business apps in 2025 requires strategic planning, sound technology choices, and a consistent commitment to user satisfaction. Whether you choose native, cross-platform, or no-code development, effective business app development is about finding the right balance between user needs, technological capabilities, and business goals. The key to successful app development is thorough preparation, thoughtful execution, and continuous improvement based on user feedback and analytical data. With the dynamic growth of the global mobile app market, the ROI potential for well-designed business apps remains high. Companies such as TTMS provide expert knowledge in AI solutions, process automation and system integration, which allows you to increase application functionality while ensuring reliable and scalable implementations tailored to business needs. It’s important to remember that launching an app is just the beginning of a longer journey that includes maintenance, updates, and development in response to changing market needs. Success requires treating app development as a continuous investment in digital transformation, not a one-off project – so that your mobile strategy delivers value for many years. If you are interested contact us now!
Read
TOP 10 AEM partners in 2025
Ranking the Best AEM Companies: Meet the Top 10 Partners for Your 2025 Projects The market for Adobe Experience Manager (AEM) implementations continues to expand as brands seek unified content management and customer‑centric digital experiences. Organisations that partner with AEM implementation partners gain access to deep technical expertise, accelerators and strategic guidance that help them move faster than competitors. Below are ten leading AEM development companies in 2025, ranked by market presence, breadth of services and overall experience. TTMS tops the list of the best Adobe Experience Manager Consulting Partners thanks to its comprehensive services, experienced consultants and innovative use of AI for content delivery. 1. Transition Technologies MS (TTMS) TTMS is a Bronze Adobe Solution Partner with one of the largest AEM competence centres in Poland and top AEM experts. The company’s philosophy emphasises personalisation and customer‑centric design: it provides end‑to‑end services covering architecture, development, maintenance and performance optimisation, and its 90‑plus consultants ensure deep expertise across all AEM modules. TTMS integrates AEM with marketing automation platforms such as Marketo, Adobe Campaign and Analytics, as well as Salesforce and customer identity systems, enabling seamless omnichannel experiences. The firm also leverages generative AI to automate tagging, translation and metadata generation, offers AI‑powered search and chatbots, and uses accelerators to reduce time‑to‑market, giving clients significant competitive advantage. Beyond core implementation, TTMS specialises in product catalogue and PIM integration. Its AEM development teams integrate existing product data into AEM’s DAM and authoring tools to eliminate manual entry errors and ensure consistent product information across channels. They also build secure customer portals on AEM that provide personalised experiences and HIPAA‑compliant document management. For organisations moving to AEM as a Cloud Service, TTMS handles performance testing, environment set‑up, integrated marketing workflows and training. Consulting services include platform audits, tailored onboarding, optimisation of legacy implementations, custom integrations and training for internal teams. Thanks to this comprehensive offering, TTMS stands out as a trusted AEM implementation partner that delivers strategic advice and innovative solutions. TTMS: company snapshot Revenues in 2024: PLN 233.7 million Number of employees: 800+ Website: https://ttms.com/aem/ Headquarters: Warsaw, Poland Main services / focus: AEM consulting & development, AI integration, PIM & product catalogue integration, customer portals, cloud migration, marketing automation integration, training and support 2. Vaimo Headquartered in Stockholm, Vaimo is a global commerce solution provider known for implementing AEM alongside Magento. The company’s strength lies in combining strategy, design and technology to build unified digital commerce platforms. Vaimo integrates AEM with e‑commerce systems and marketing automation tools, enabling brands to manage content and product data across multiple channels. Its expertise in user experience, technical architecture and performance optimisation positions Vaimo as a reliable AEM implementation partner for retailers seeking personalised shopping experiences. Vaimo: company snapshot Revenues in 2024: Undisclosed Number of employees: 500+ Website: www.vaimo.com Headquarters: Stockholm, Sweden Main services / focus: AEM & Magento integration, digital commerce platforms, design & strategy, omnichannel experiences 3. Appnovation Appnovation is a full‑service digital consultancy with offices in North America, Europe and Asia. The firm combines digital strategy, experience design and technology to deliver enterprise‑grade AEM solutions. Appnovation’s multidisciplinary teams develop multi‑channel content architectures, integrate analytics and marketing automation tools, and provide managed services to optimise clients’ AEM platforms. Its global presence and user‑centric design approach make Appnovation a popular AEM development company for organisations pursuing large‑scale digital transformation. Appnovation: company snapshot Revenues in 2024: Undisclosed Number of employees: 600+ Website: www.appnovation.com Headquarters: Vancouver, Canada Main services / focus: AEM implementation, user‑experience design, digital strategy, cloud‑native development, managed services 4. Magneto IT Solutions Magneto IT Solutions specialises in building e‑commerce platforms and digital experiences for retail brands. It leverages Adobe Experience Manager to create scalable, content‑driven websites and integrates AEM with Magento, Shopify and other commerce platforms. The company’s strong focus on design and conversion optimisation helps clients deliver seamless shopping experiences. Magneto’s ability to customise AEM for specific retail verticals positions it among the top AEM implementation partners for online stores. Magneto IT Solutions: company snapshot Revenues in 2024: Undisclosed Number of employees: 200+ Website: www.magnetoitsolutions.com Headquarters: Ahmedabad, India Main services / focus: AEM development for retail, e‑commerce integration, UX/UI design, digital marketing 5. Akeneo Akeneo is recognised for its product information management (PIM) platform and its synergy with AEM. The company enables brands to centralise and enrich product data, then syndicate it to AEM to ensure consistency across digital channels. By integrating AEM with its PIM tool, Akeneo helps organisations streamline product catalogue management, reduce manual entry and improve data accuracy. This focus on product data integrity makes Akeneo an important partner for companies using AEM in commerce and manufacturing. Akeneo: company snapshot Revenues in 2024: Undisclosed Number of employees: 400+ Website: www.akeneo.com Headquarters: Nantes, France Main services / focus: Product information management, AEM & PIM integration, digital commerce solutions 6. Codal Codal is a design‑driven digital agency that combines user experience research with robust engineering. The firm adopts a user‑centric approach to AEM implementations, ensuring that information architecture, component design and content workflows meet both customer and business needs. Codal’s teams also integrate data analytics and marketing automation platforms with AEM, enabling clients to make informed decisions and deliver personalised experiences. This design‑first ethos makes Codal a top choice for brands looking to align aesthetics and technology. Codal: company snapshot Revenues in 2024: Undisclosed Number of employees: 250+ Website: www.codal.com Headquarters: Chicago, USA Main services / focus: AEM implementation, UX/UI design, data analytics, integration services 7. Synecore Synecore is a UK‑based digital marketing agency that blends inbound marketing strategies with AEM’s powerful content management capabilities. It helps clients craft inbound campaigns, develop content strategies and integrate marketing automation tools with AEM. Synecore’s team ensures that content, design and technical implementations support lead generation and customer engagement. Its expertise in inbound marketing and content strategy positions Synecore as a valuable AEM development company for organisations seeking to combine marketing and technology. Synecore: company snapshot Revenues in 2024: Undisclosed Number of employees: 50+ Website: www.synecore.co.uk Headquarters: London, UK Main services / focus: Inbound marketing, content strategy, AEM implementation, marketing automation integration 8. Mageworx Mageworx is best known for its Magento extensions, but the company also offers AEM integration services for e‑commerce sites. By connecting AEM with Magento and other e‑commerce platforms, Mageworx enables brands to manage product information and content in one environment. The company develops custom modules, optimises website performance and provides SEO and analytics integration to drive online sales. For merchants looking to leverage AEM within a Magento ecosystem, Mageworx is a solid partner. Mageworx: company snapshot Revenues in 2024: Undisclosed Number of employees: 100+ Website: www.mageworx.com Headquarters: Minneapolis, USA Main services / focus: Magento extensions, AEM integration, performance optimisation, SEO & analytics 9. Spargo Spargo is a Polish digital transformation firm focusing on commerce, content and marketing technologies. It uses AEM to deliver integrated digital experiences for clients in retail, finance and media. Spargo combines product information management, marketing automation and e‑commerce integrations to help brands operate efficiently across multiple channels. With its cross‑platform expertise and agile methodology, Spargo stands out among regional AEM implementation partners. Spargo: company snapshot Revenues in 2024: Undisclosed Number of employees: 100+ Website: www.spargo.pl Headquarters: Warsaw, Poland Main services / focus: Digital commerce solutions, AEM development, PIM integration, marketing automation 10. Divante Divante is an e‑commerce software house and innovation partner based in Poland. It has strong expertise in Magento, Pimcore and AEM, and builds headless commerce architectures that allow clients to deliver content across multiple devices and channels. Divante’s teams focus on open‑source technologies, API‑first approaches and custom integrations, enabling rapid experimentation and scalability. The company’s community‑driven culture and technical depth make it a trusted partner for enterprises looking to modernise their digital commerce stack. Divante: company snapshot Revenues in 2024: Undisclosed Number of employees: 300+ Website: www.divante.com Headquarters: Wrocław, Poland Main services / focus: Headless commerce, AEM development, open‑source platforms, custom integrations Our AEM Case Studies: Proven Expertise in Action At TTMS, we believe that real results speak louder than promises. Below you will find selected case studies that illustrate how our team successfully delivers AEM consulting, migrations, integrations, and AI-driven optimizations for global clients across various industries Migrating to Adobe EDS – We successfully migrated a complex ecosystem into Adobe EDS, ensuring seamless data flow and robust scalability. The project minimized downtime and prepared the client for future growth. Adobe Analytics Integration with AEM – TTMS integrated Adobe Analytics with AEM to deliver actionable insights for marketing and content teams. This improved customer experience tracking and enabled data-driven decision-making. Integration of PingOne and Adobe AEM – We implemented secure identity management by integrating PingOne with AEM. The solution strengthened authentication and improved user experience across digital platforms. AI SEO Meta Optimization – By applying AI-driven SEO optimization in AEM, we boosted the client’s search visibility and organic traffic. The approach delivered measurable improvements in engagement and rankings. AEM Cloud Migration for a Watch Manufacturer – TTMS migrated a luxury watch brand’s digital ecosystem into AEM Cloud. The move improved performance, reduced costs, and enabled long-term scalability. Migration from Adobe LiveCycle to AEM Forms – We replaced legacy Adobe LiveCycle with modern AEM Forms, improving usability and efficiency. This allowed the client to streamline processes and reduce operational risks. Headless CMS Architecture for Multi-App Delivery – TTMS designed a headless CMS approach for seamless content delivery across multiple apps. The solution increased flexibility and accelerated time-to-market. Pharma Design System & Template Unification – We developed a unified design system for a global pharma leader. It improved brand consistency and reduced development costs across international teams. Accelerating Adobe Delivery through Expert Intervention – Our experts accelerated stalled Adobe projects, delivering results faster and more efficiently. The intervention saved resources and increased project success rates. Comprehensive Digital Audit for Strategic Clarity – TTMS conducted an in-depth digital audit that revealed key optimization areas. The client gained actionable insights and a roadmap for long-term success. Expert-Guided Content Migration – We supported a smooth transition to a new platform through structured content migration. This minimized risks and ensured business continuity during change. Global Patient Portal Improvement – TTMS enhanced a global medical portal by simplifying medical terminology for patients. The upgrade improved accessibility, patient satisfaction, and global adoption. If you want to learn how we can bring the same success to your AEM projects, our team is ready to help. Get in touch with TTMS today and let’s discuss how we can accelerate your digital transformation journey together. What makes a good AEM implementation partner in 2025? A good AEM implementation partner in 2025 is not only a company with certified Adobe Experience Manager expertise, but also one that can combine consulting, cloud migration, integration, and AI-driven solutions. The best partners deliver both technical precision and business alignment, ensuring that the implementation supports digital transformation goals. What really distinguishes the top firms today is their ability to integrate AEM with analytics, identity management, and personalization engines. This creates a scalable, secure, and customer-focused digital platform that drives measurable business value. How do I compare different AEM development companies? How to compare the best Adobe AEM implementation companies? When comparing AEM development companies, it is essential to look beyond price and consider factors such as their proven track record, the number of certified AEM developers, and the industries they serve. A reliable partner will provide transparency about previous projects, case studies, and long-term support models. It is also worth checking if the company is experienced in AEM Cloud Services, as many enterprises are migrating away from on-premises solutions. Finally, cultural fit and communication style play a huge role in successful collaborations, especially for global organizations. Is it worth choosing a local AEM consulting partner over a global provider? The decision between a local and a global AEM consulting partner depends on your organization’s priorities. A local partner may offer closer cultural alignment, time zone convenience, and faster on-site support. On the other hand, global providers often bring broader expertise, larger teams, and experience with complex multinational implementations. Many businesses in 2025 follow a hybrid approach, where they choose a mid-sized international AEM company that combines the flexibility of local service with the scalability of a global player. How much does it cost to implement AEM with a professional partner? The cost of implementing Adobe Experience Manager with a professional partner varies significantly depending on the project’s scale, complexity, and integrations required. For smaller projects, costs may start from tens of thousands of euros, while large-scale enterprise implementations can easily exceed several hundred thousand euros. What matters most is the return on investment – a skilled AEM partner will optimize content workflows, personalization, and data-driven marketing, generating long-term business value that outweighs the initial spend. Choosing the right partner ensures predictable timelines and reduced risk of costly delays. What are the latest trends in AEM implementations in 2025? In 2025, the hottest trends in AEM implementations revolve around AI integration, headless CMS architectures, and cloud-native deployments. Companies increasingly expect their AEM platforms to be fully compatible with AI-powered personalization, predictive analytics, and automated SEO optimization. Headless CMS setups are gaining momentum because they allow content to be delivered seamlessly across web, mobile, and IoT applications. At the same time, more organizations are moving to AEM Cloud Services, reducing infrastructure overhead while ensuring continuous updates and scalability. These trends highlight the need for AEM implementation partners who can innovate while maintaining enterprise-grade stability.
Read
Microsoft’s In-House AI Move: MAI-1 and MAI-Voice-1 Signal a Shift from OpenAI
Microsoft’s In-House AI Move: MAI-1 and MAI-Voice-1 Signal a Shift from OpenAI August 2025 – Microsoft has unveiled two internally developed AI models – MAI-1 (a new large language model) and MAI-Voice-1 (a speech generation model) – marking a strategic pivot toward technological independence from OpenAI. After years of leaning on OpenAI’s models (and investing around $13 billion in that partnership since 2019), Microsoft’s AI division is now striking out on its own with homegrown AI capabilities. This move signals that despite its deep ties to OpenAI, Microsoft is positioning itself to have more direct control over the AI technology powering its products – a development with big implications for the industry. A Strategic Pivot Away from OpenAI Microsoft’s announcement of MAI-1 and MAI-Voice-1 – made in late August 2025 – is widely seen as a bid for greater self-reliance in AI. Industry observers note that this “proprietary” turn represents a pivot away from dependence on OpenAI. For years, OpenAI’s GPT-series models (like GPT-4) have been the brains behind many Microsoft products (from Azure OpenAI services to GitHub Copilot and Bing’s chat). However, tensions have emerged in the collaboration. OpenAI has grown into a more independent (and highly valued) entity, and Microsoft reportedly “openly criticized” OpenAI’s GPT-4 as “too expensive and slow” for certain consumer needs. Microsoft even quietly began testing other AI models for its Copilot services, signaling concern about over-reliance on a single partner. In early 2024, Microsoft hired Mustafa Suleyman (co-founder of DeepMind and former Inflection AI CEO) to lead a new internal AI team – a clear sign it intended to develop its own models. Suleyman has since emphasized “optionality” in Microsoft’s AI strategy: the company will use the best models available – whether from OpenAI, open-source, or its own lab – routing tasks to whichever model is most capable. The launch of MAI-1 and MAI-Voice-1 puts substance behind that strategy. It gives Microsoft a viable in-house alternative to OpenAI’s tech, even as the two remain partners. In fact, Microsoft’s AI leadership describes these models as augmenting (not immediately replacing) OpenAI’s – for now. But the long-term trajectory is evident: Microsoft is preparing for a post-OpenAI future in which it isn’t beholden to an external supplier for core AI innovations. As one Computerworld analysis put it, Microsoft didn’t hire a visionary AI team “simply to augment someone else’s product” – it’s laying groundwork to eventually have its own AI foundation. Meet MAI-1 and MAI-Voice-1: Microsoft’s New AI Models MAI-Voice-1 is Microsoft’s first high-performance speech generation model. The company says it can generate a full minute of natural-sounding audio in under one second on a single GPU, making it “one of the most efficient speech systems” available. In practical terms, MAI-Voice-1 gives Microsoft a fast, expressive text-to-speech engine under its own roof. It’s already powering user-facing features: for example, the new Copilot Daily service has an AI news host that reads top stories to users in a natural voice, and a Copilot Podcasts feature can create on-the-fly podcast dialogues from text prompts – both driven by MAI-Voice-1’s capabilities. Microsoft touts the model’s high fidelity and expressiveness across single- and multi-speaker scenarios. In an era where voice interfaces are rising, Microsoft clearly views this as strategic tech (the company even said “voice is the interface of the future” for AI companions). Notably, OpenAI’s own foray into audio has been Whisper, a model for speech-to-text transcription – but OpenAI hasn’t productized a comparable text-to-speech model. With MAI-Voice-1, Microsoft is filling that gap by offering AI that can speak to users with human-like intonation and speed, without relying on a third-party engine. MAI-1 (Preview) is Microsoft’s new large language model (LLM) for text, and it represents the company’s first internally trained foundation model. Under the hood, MAI-1 uses a mixture-of-experts architecture and was trained (and post-trained) on roughly 15,000 NVIDIA H100 GPUs. (For context, that is a substantial computing effort, though still more modest than the 100,000+ GPU clusters reportedly used to train some rival frontier models.) The model is designed to excel at instruction-following and helpful responses to everyday queries – essentially, the kind of general-purpose assistant tasks that GPT-4 and similar models handle. Microsoft has begun publicly testing MAI-1 in the wild: it was released as MAI-1-preview on LMArena, a community benchmarking platform where AI models can be compared head-to-head by users. This allows Microsoft to transparently gauge MAI-1’s performance against other AI models (competitors and open models alike) and iterate quickly. According to Microsoft, MAI-1 is already showing “a glimpse of future offerings inside Copilot” – and the company is rolling it out selectively into Copilot (Microsoft’s AI assistant suite across Windows, Office, and more) for tasks like text generation. In coming weeks, certain Copilot features will start using MAI-1 for handling user queries, with Microsoft collecting feedback to improve the model. In short, MAI-1 is not yet replacing OpenAI’s GPT-4 within Microsoft’s products, but it’s on a path to eventually play a major role. It gives Microsoft the ability to tailor and optimize an LLM specifically for its ecosystem of “Copilot” assistants. How do these models stack up against OpenAI’s? In terms of capabilities, OpenAI’s GPT-4 (and the newly released GPT-5) still set the bar in many domains, from advanced reasoning to code generation. Microsoft’s MAI-1 is a first-generation effort by comparison, and Microsoft itself acknowledges it is taking an “off-frontier” approach – aiming to be a close second rather than the absolute cutting edge. “It’s cheaper to give a specific answer once you’ve waited for the frontier to go first… that’s our strategy, to play a very tight second,” Suleyman said of Microsoft’s model efforts. The architecture choices also differ: OpenAI has not disclosed GPT-4’s architecture, but it is believed to be a giant transformer model utilizing massive compute resources. Microsoft’s MAI-1 explicitly uses a mixture-of-experts design, which can be more compute-efficient by activating different “experts” for different queries. This design, plus the somewhat smaller training footprint, suggests Microsoft may be aiming for a more efficient, cost-effective model – even if it’s not (yet) the absolute strongest model on the market. Indeed, one motivation for MAI-1 was likely cost/control: Microsoft found that using GPT-4 at scale was expensive and sometimes slow, impeding consumer-facing uses. By owning a model, Microsoft can optimize it for latency and cost on its own infrastructure. On the voice side, OpenAI’s Whisper model handles speech recognition (transcribing audio to text), whereas Microsoft’s MAI-Voice-1 is all about speech generation (producing spoken audio from text). This means Microsoft now has an in-house solution for giving its AI a “voice” – an area where it previously relied on third-party text-to-speech services or less flexible solutions. MAI-Voice-1’s standout feature is its speed and efficiency (near real-time audio generation), which is crucial for interactive voice assistants or reading long content aloud. The quality is described as high fidelity and expressive, aiming to surpass the often monotone or robotic outputs of older-generation TTS systems. In essence, Microsoft is assembling its own full-stack AI toolkit: MAI-1 for text intelligence, and MAI-Voice-1 for spoken interaction. These will inevitably be compared to OpenAI’s GPT-4 (text) and the various voice AI offerings in the market – but Microsoft now has the advantage of deeply integrating these models into its products and tuning them as it sees fit. Implications for Control, Data, and Compliance Beyond technical specs, Microsoft’s in-house AI push is about control – over the technology’s evolution, data, and alignment with company goals. By developing its own models, Microsoft gains a level of ownership that was impossible when it solely depended on OpenAI’s API. As one industry briefing noted, “Owning the model means owning the data pipeline, compliance approach, and product roadmap.” In other words, Microsoft can now decide how and where data flows in the AI system, set its own rules for governance and regulatory compliance, and evolve the AI functionality according to its own product timeline, not someone else’s. This has several tangible implications: Data governance and privacy: With an in-house model, sensitive user data can be processed within Microsoft’s own cloud boundaries, rather than being sent to an external provider. Enterprises using Microsoft’s AI services may take comfort that their data is handled under Microsoft’s stringent enterprise agreements, without third-party exposure. Microsoft can also more easily audit and document how data is used to train or prompt the model, aiding compliance with data protection regulations. This is especially relevant as new AI laws (like the EU’s AI Act) demand transparency and risk controls – having the AI “in-house” could simplify compliance reporting since Microsoft has end-to-end visibility into the model’s operation. Product customization and differentiation: Microsoft’s products can now get bespoke AI enhancements that a generic OpenAI model might not offer. Because Microsoft controls MAI-1’s training and tuning, it can infuse the model with proprietary knowledge (for example, training on Windows user support data to make a better helpdesk assistant) or optimize it for specific scenarios that matter to its customers. The Copilot suite can evolve with features that leverage unique model capabilities Microsoft builds (for instance, deeper integration with Microsoft 365 data or fine-tuned industry versions of the model for enterprise customers). This flexibility in shaping the roadmap is a competitive differentiator – Microsoft isn’t limited by OpenAI’s release schedule or feature set. As Launch Consulting emphasized to enterprise leaders, relying on off-the-shelf AI means your capabilities are roughly the same as your competitors’; owning the model opens the door to unique features and faster iterations. Compliance and risk management: By controlling the AI models, Microsoft can more directly enforce compliance with ethical AI guidelines and industry regulations. It can build in whatever content filters or guardrails it deems necessary (and adjust them promptly as laws change or issues arise), rather than being subject to a third party’s policies. For enterprises in regulated sectors (finance, healthcare, government), this control is vital – they need to ensure AI systems comply with sector-specific rules. Microsoft’s move could eventually allow it to offer versions of its AI that are certified for compliance, since it has full oversight. Moreover, any concerns about how AI decisions are made (transparency, bias mitigation, etc.) can be addressed by Microsoft’s own AI safety teams, potentially in a more customized way than OpenAI’s one-size-fits-all approach. In short, Microsoft owning the AI stack could translate to greater trust and reliability for enterprise customers who must answer to regulators and risk officers. It’s worth noting that Microsoft is initially applying MAI-1 and MAI-Voice-1 in consumer-facing contexts (Windows, Office 365 Copilot for end-users) and not immediately replacing the AI inside enterprise products. Suleyman himself commented that the first goal was to make something that works extremely well for consumers – leveraging Microsoft’s rich consumer telemetry and data – essentially using the broad consumer usage to train and refine the models. However, the implications for enterprise clients are on the horizon. We can expect that as these models mature, Microsoft will integrate them into its Azure AI offerings and enterprise Copilot products, offering clients the option of Microsoft’s “first-party” models in addition to OpenAI’s. For enterprise decision-makers, Microsoft’s pivot sends a clear message: AI is becoming core intellectual property, and owning or selectively controlling that IP can confer advantages in data governance, customization, and compliance that might be hard to achieve with third-party AI alone. Build Your Own or Buy? Lessons for Businesses Microsoft’s bold move raises a key question for other companies: Should you develop your own AI models, or continue relying on foundation models from providers like OpenAI or Anthropic? The answer will differ for each organization, but Microsoft’s experience offers some valuable considerations for any business crafting its AI strategy: Strategic control vs. dependence: Microsoft’s case illustrates the risk of over-dependence on an external AI provider. Despite a close partnership, Microsoft and OpenAI had diverging interests (even reportedly clashing over what Microsoft gets out of its big investment). If an AI capability is mission-critical to your business or product, relying solely on an outside vendor means your fate is tied to their decisions, pricing, and roadmap changes. Building your own model (or acquiring the talent to) gives you strategic independence. You can prioritize the features and values important to you without negotiating with a third party. However, it also means shouldering all the responsibility for keeping that model state-of-the-art. Resources and expertise required: On the flip side, few companies have the deep pockets and AI research muscle that Microsoft does. Training cutting-edge models is extremely expensive – Microsoft’s MAI-1 used 15,000 high-end GPUs just for its preview model, and the leading frontier models use even larger compute budgets. Beyond hardware, you need scarce AI research talent and large-scale data to train a competitive model. For most enterprises, it’s simply not feasible to replicate what OpenAI, Google, or Microsoft are doing at the very high end. If you don’t have the scale to invest in tens of millions (or more likely, hundreds of millions) of dollars in AI R&D, leveraging a pre-built foundation model might yield a far better ROI. Essentially, build if AI is a core differentiator you can substantially improve – but buy if AI is a means to an end and others can provide it more cheaply. Privacy, security, and compliance needs: A major driver for some companies to consider “rolling their own” AI is data sensitivity and compliance. If you operate in a field with strict data governance (say, patient health data, or confidential financial info), sending data to a third-party AI API – even with promises of privacy – might be a non-starter. An in-house model that you can deploy in a secure environment (or at least a model from a vendor willing to isolate your data) could be worth the investment. Microsoft’s move shows an example of prioritizing data control: by handling AI internally, they keep the whole data pipeline under their policies. Other firms, too, may decide that owning the model (or using an open-source model locally) is the safer path for compliance. That said, many AI providers are addressing this by offering on-premises or dedicated instances – so explore those options as well. Need for customization and differentiation: If the available off-the-shelf AI models don’t meet your specific needs or if using the same model as everyone else diminishes your competitive edge, building your own can be attractive. Microsoft clearly wanted AI tuned for its Copilot use cases and product ecosystem – something it can do more freely with in-house models. Likewise, other companies might have domain-specific data or use cases (e.g. a legal AI assistant, or an industrial AI for engineering data) where a general model underperforms. In such cases, investing in a proprietary model or at least a fine-tuned version of an open-source model could yield superior results for your niche. We’ve seen examples like Bloomberg GPT – a financial domain LLM trained on finance data – which a company built to get better finance-specific performance than generic models. Those successes hint that if your data or use case is unique enough, a custom model can provide real differentiation. Hybrid approaches – combine the best of both: Importantly, choosing “build” versus “buy” isn’t all-or-nothing. Microsoft itself is not abandoning OpenAI entirely; the company says it will “continue to use the very best models from [its] team, [its] partners, and the latest innovations from the open-source community” to power different features. In practice, Microsoft is adopting a hybrid model – using its own AI where it adds value, but also orchestrating third-party models where they excel, thereby delivering the best outcomes across millions of interactions. Other enterprises can adopt a similar strategy. For example, you might use a general model like OpenAI’s for most tasks, but switch to a privately fine-tuned model when handling proprietary data or domain-specific queries. There are even emerging tools to help route requests to different models dynamically (the way Microsoft’s “orchestrator” does). This approach allows you to leverage the immense investment big AI providers have made, while still maintaining options to plug in your own specialty models for particular needs. Bottom line: Microsoft’s foray into building MAI-1 and MAI-Voice-1 underscores that AI has become a strategic asset worth investing in – but it also demonstrates the importance of balancing innovation with practical business needs. Companies should re-evaluate their build-vs-buy AI strategy, especially if control, privacy, or differentiation are key drivers. Not every organization will choose to build a giant AI model from scratch (and most shouldn’t). Yet every organization should consider how dependent it wants to be on external AI providers and whether owning certain AI capabilities could unlock more value or mitigate risks. Microsoft’s example shows that with sufficient scale and strategic need, developing one’s own AI is not only possible but potentially transformative. For others, the lesson may be to negotiate harder on data and compliance terms with AI vendors, or to invest in smaller-scale bespoke models that complement the big players. In the end, Microsoft’s announcement is a landmark in the AI landscape: a reminder that the AI ecosystem is evolving from a few foundation-model providers toward a more heterogeneous field. For business leaders, it’s a prompt to think of AI not just as a service you consume, but as a capability you cultivate. Whether that means training your own models, fine-tuning open-source ones, or smartly leveraging vendor models, the goal is the same – align your AI strategy with your business’s unique needs for agility, trust, and competitive advantage in the AI era. Supporting Your AI Journey: Full-Spectrum AI Solutions from TTMS As the AI ecosystem evolves, TTMS offers AI Solutions for Business – a comprehensive service line that guides organizations through every stage of their AI strategy, from deploying pre-built models to developing proprietary ones. Whether you’re integrating AI into existing workflows, automating document-heavy processes, or building large-scale language or voice models, TTMS has capabilities to support you. For law firms, our AI4Legal specialization helps automate repetitive tasks like contract drafting, court transcript analysis, and document summarizations—all while maintaining data security and compliance. For customer-facing and sales-driven sectors, our Salesforce AI Integration service embeds generative AI, predictive insights, and automation directly into your CRM, helping improve user experience, reduce manual workload, and maintain control over data. If Microsoft’s move to build its own models signals one thing, it’s this: the future belongs to organizations that can both buy and build intelligently – and TTMS is ready to partner with you on that path. Why is Microsoft creating its own AI models when it already partners with OpenAI? Microsoft values the access it has to OpenAI’s cutting-edge models, but building MAI-1 and MAI-Voice-1 internally gives it more control over costs, product integration, and regulatory compliance. By owning the technology, Microsoft can optimize for speed and efficiency, protect sensitive data within its own infrastructure, and develop features tailored specifically to its ecosystem. This reduces dependence on a single provider and strengthens Microsoft’s long-term strategic position. How do Microsoft’s MAI-1 and MAI-Voice-1 compare with OpenAI’s models? MAI-1 is a large language model designed to rival GPT-4 in text-based tasks, but Microsoft emphasizes efficiency and integration rather than pushing absolute frontier performance. MAI-Voice-1 focuses on ultra-fast, natural-sounding speech generation, which complements OpenAI’s Whisper (speech-to-text) rather than duplicating it. While OpenAI still leads in some benchmarks, Microsoft’s models give it flexibility to innovate and align development closely with its own products. What are the risks for businesses in relying solely on third-party AI providers? Total dependence on external AI vendors creates exposure to pricing changes, roadmap shifts, or availability issues outside a company’s control. It can also complicate compliance when sensitive data must flow through a third party’s systems. Businesses risk losing differentiation if they rely on the same model that competitors use. Microsoft’s decision highlights these risks and shows why strategic independence in AI can be valuable. hat lessons can other enterprises take from Microsoft’s pivot? Not every company can afford to train a model on thousands of GPUs, but the principle is scalable. Organizations should assess which AI capabilities are core to their competitive advantage and consider building or fine-tuning models in those areas. For most, a hybrid approach – combining foundation models from providers with domain-specific custom models – strikes the right balance between speed, cost, and control. Microsoft demonstrates that owning at least part of the AI stack can pay dividends in trust, compliance, and differentiation. Will Microsoft continue to use OpenAI’s technology after launching its own models? Yes. Microsoft has been clear that it will use the best model for the task, whether from OpenAI, the open-source community, or its internal MAI family. The launch of MAI-1 and MAI-Voice-1 doesn’t replace OpenAI overnight; it creates options. This “multi-model” strategy allows Microsoft to route workloads dynamically, ensuring it can balance performance, cost, and compliance. For business leaders, it’s a reminder that AI strategies don’t need to be all-or-nothing – flexibility is a strength.
Read
The Cyber Resilience Act in the energy sector – obligations, risks, and how to prepare for 2025?
The EU’s Cyber Resilience Act (CRA) marks a turning point in the way digital products are secured across Europe. By 2027, all software will need to comply with CRA requirements, and as early as next year, companies will face mandatory cybersecurity incident reporting. This issue is particularly critical for the energy sector, where outdated and poorly secured systems are still in use. A lack of proper safeguards can lead to severe consequences – not only financial but also operational and social. CRA applies to all software in the EU starting in 2027. For the energy sector, this means obligations such as SBOM, secure-by-design, and incident reporting. TTMS supports companies in preparing for and implementing CRA requirements. Ignoring the regulation may result in fines, market exclusion, and exposure to real cyberattacks. 1. Why is the energy sector especially vulnerable? The energy sector is the backbone of modern society – the economy, public administration, and daily life all depend on its stability. As critical infrastructure, electricity supply must be uninterrupted. Any disruption can cause serious social and economic fallout – from halting transport and communications to crippling hospitals or emergency services. Yet, this infrastructure relies on complex control systems such as SCADA, RTU, EMS, or HMI. Many of them were designed in an era when cybersecurity was not a top design priority. Built primarily for performance and reliability, they are often ill-equipped to withstand today’s digital threats. The challenge intensifies with the convergence of OT and IT systems. More elements of physical infrastructure are now connected to corporate networks, increasing the attack surface and complicating risk management. Cybercriminals no longer need physical access to a power plant or substation – a single vulnerability in a remote-control system may be enough. Adding to the risk is technological legacy. Many organisations still rely on outdated operating systems and applications deeply embedded in technological processes. These cannot be easily updated or replaced, making them an easy target for cyberattacks. 1.1 The threat is not theoretical – real incidents prove it. In 2017, a cyberattack targeted the German company Netcom BW, a telecommunications network operator owned by EnBW, one of Germany’s largest energy providers. The attacker was a Russian national and a member of Berserk Bear, a group linked to Russia’s FSB intelligence service. The goal was to infiltrate communication infrastructure used not only by Netcom BW but also by energy system operators. While the companies assured that the core energy infrastructure remained intact, the attack exposed vulnerabilities in the supply chain and the dependencies between IT systems and critical energy assets. This is a warning that cannot be ignored. Incidents like this highlight that cybersecurity cannot stop at the boundaries of a power plant or transmission grid – it must extend to technology suppliers, communication systems, and all interconnected digital components. This is precisely why the implementation of the EU’s Cyber Resilience Act is not only a legal requirement but also a strategic step towards building a resilient energy sector for the future. 2. CRA – What Does It Mean for Energy Companies and How Can TTMS Help? The new EU regulation introduced by the Cyber Resilience Act (CRA) imposes binding cybersecurity obligations on software providers across the energy sector. For many organisations, this means reorganising development processes, implementing new tools, and ensuring both formal and technical compliance. This is where Transition Technologies MS steps in, offering both advisory and technological support. 2.1 Mandatory SBOMs (Software Bill of Materials) CRA requires every company delivering software to maintain a complete list of components, libraries, and dependencies used in their product. How TTMS helps: We implement tools that automate the creation and updating of SBOMs in popular formats (e.g. SPDX, CycloneDX), integrating them with CI/CD pipelines. We also support risk analysis of open-source components and help establish dependency management policies. 2.2 Secure-by-Design Development CRA enforces the obligation to embed security into products from the very first design stage. How TTMS helps: We provide threat modelling workshops, application architecture security audits, and the implementation of secure DevSecOps practices. Our support also includes penetration testing and code reviews at every stage of the product lifecycle. 2.3 Vulnerability Management The regulation requires organisations to detect, classify, and patch vulnerabilities quickly – not only in their own code but also in third-party components. How TTMS helps: We build and integrate vulnerability management processes – from static scanning (SAST) and dynamic testing (DAST) to real-time vulnerability monitoring systems. We help implement procedures aligned with best practices (e.g. CVSS, CVD). 2.4 Incident Reporting Every major security incident must be reported to ENISA or the local CSIRT within 24 hours. How TTMS helps: We create incident response plans (IRPs), implement detection and automated reporting systems, and train IT and OT teams in CRA-compliant procedures. TTMS can also act as an external cyber emergency response partner. 2.5 EU Declaration of Conformity Software providers must deliver a formal document confirming compliance with CRA requirements – this is not only a declaration but also a legal responsibility. How TTMS helps: We support companies in creating and maintaining CRA-required documentation, including declarations of conformity, security policies, and technical support plans. We provide pre-implementation audits and assistance in preparing for regulatory inspections. 2.6 Additional Support and Parallel Development Implementing CRA requirements does not have to mean halting other development projects. At TTMS, we provide additional resources in a staff augmentation model, enabling organisations to continue software development in parallel with the process of adapting applications to new regulations. This way, energy companies can maintain their pace of innovation while effectively meeting legal requirements. Moreover, we offer comprehensive cybersecurity testing support across three key areas: Infrastructure audits and penetration testing Application audits and penetration testing Source code audits All these services are delivered by TTMS in cooperation with Transition Technologies Software (TTSW), ensuring complete security both at the system and application level. Why Work with TTMS? Proven experience in the energy sector – deep knowledge of SCADA, EMS, DMS, and OT/IT environments. Dedicated Quality and Cybersecurity experts – supporting organisations throughout the entire CRA compliance cycle. Ready-to-use solutions and tools – from SBOM management to incident response and risk analysis. Security-as-a-Service – flexible support models tailored to client needs. 3. Ignoring CRA Could Cost More Than You Think Non-compliance with the Cyber Resilience Act is not just a formal issue – it is a real risk to business continuity and market presence in the EU. CRA foresees severe financial penalties – up to €15 million or 2.5% of global annual turnover – for failing to meet software security requirements. In addition, non-compliant products may be completely excluded from the EU market, which for many companies – especially those in critical infrastructure – could mean the loss of key contracts. Neglecting security also increases the risk of real cyberattacks that may paralyse systems, leak sensitive data, and cause massive financial and reputational losses. A notable example is the ransomware attack on the Norwegian company Norsk Hydro in March 2019. The global aluminium producer and energy provider had its IT systems worldwide shut down, forcing plants to switch to manual operations. The direct and indirect costs exceeded $70 million, and the company struggled for weeks to restore operations and rebuild market trust. Although this case dates back a few years, the number of similar attacks has been rising steadily amid Europe’s ongoing hybrid warfare. In 2025, Poland reported two major cybersecurity incidents in public institutions – one involving a personal data breach caused by an email system intrusion, and another targeting industrial control systems. Cases like these show that failing to act proactively on cybersecurity can cost far more than investing in CRA compliance. It is not only a legal obligation but also a condition for maintaining competitiveness and business resilience in the digital era. 4. Cyber Resilience Act – Consequences of Non-Compliance and Real Risks of Cyberattacks Failure to comply with CRA can result in: Financial penalties of up to €15 million or 2.5% of global annual turnover Exclusion from the EU market Increased risk of cyberattacks leading to system paralysis and massive financial losses 4.1 When Should You Start Acting? The Clock Is Ticking The Cyber Resilience Act was adopted in October 2024. While full compliance will not be required until December 2027, one of the key obligations – reporting security incidents within 24 hours – will already apply from September 2026. This means that companies – especially those in critical infrastructure sectors such as energy – have less than a year to prepare procedures, train teams, implement the right tools, and test their systems. Implementing CRA is not about a single document – it requires a comprehensive change in how software is developed and maintained, covering security, documentation, vulnerability management, and formal compliance. Leaving compliance until the last minute is a recipe for errors, system gaps, and costly consequences. Organisations that start preparing now will gain not only a time advantage but also a strategic one, demonstrating to partners and customers that they take cybersecurity seriously – before being forced to. This is precisely where Transition Technologies MS (TTMS) can make the difference. Our expert teams support organisations at every stage of CRA readiness – from analysing current processes and conducting security audits, to implementing SBOM and vulnerability management tools, developing incident reporting procedures, and preparing formal compliance documentation. TTMS does more than advise – we implement real technical solutions, deliver training, and provide ongoing support as part of a long-term partnership. If your organisation operates in the energy sector, do not delay CRA compliance – the consequences of inaction can be severe both operationally and financially. Talk to one of our cybersecurity experts and discover how TTMS can help you navigate this process smoothly and effectively. Visit ttms.pl/energy to learn more about the software and solutions we build for energy companies. Looking for a quick summary? Check out our FAQ section, where we have gathered the most important questions and answers from this article. When does the Cyber Resilience Act (CRA) come into force and what is the timeline? The Cyber Resilience Act was officially adopted in October 2024. Full compliance with its provisions will be mandatory from December 2027. However, from September 2026, companies will already be required to report security incidents within 24 hours. This leaves limited time for organisations to analyse, prepare, and implement the necessary processes – especially in the energy sector, where action must be both fast and methodical. Which products and systems in the energy sector are covered by CRA? The regulation applies to all “products with digital elements,” meaning both physical devices and software that can connect to a network. In practice, this includes critical energy management and control systems such as SCADA, RTU, EMS, DMS, and HMI – the backbone of digital energy infrastructure. If your software operates in this environment, CRA directly affects your organisation. What specific obligations does CRA impose on energy companies? Energy companies must introduce Software Bills of Materials (SBOMs), design systems with a secure-by-design approach, manage and patch vulnerabilities quickly, report major incidents to relevant institutions within strict deadlines, and prepare an EU Declaration of Conformity for their products. These are not mere formalities – they have a tangible impact on the security and resilience of entire energy systems. What are the risks for companies that ignore CRA requirements? Non-compliance may result in fines of up to €15 million or 2.5% of a company’s global annual turnover – whichever is higher. In addition, non-compliant products may be removed from the EU market entirely. Beyond financial penalties, ignoring CRA also exposes companies to real cyber risks, such as ransomware attacks. The Norsk Hydro case showed how a single incident can cause operational paralysis, data loss, and reputational damage with long-term consequences. Does every company have to report incidents, even if there was no service disruption? Yes. CRA requires reporting of any major security incident or actively exploited vulnerability within 24 hours of detection. A follow-up report must then be submitted within 72 hours, and a final summary within 14 days. This applies not only to incidents that cause outages but also to those that could potentially affect product or user security. The aim is to ensure early transparency and rapid mitigation across the entire EU market.
Read
TOP 7 AI Solutions Delivery Companies in 2025
TOP 7 AI Solutions Delivery Companies in 2025 – Global Ranking of Leading Providers In 2025, artificial intelligence is more than a tech buzzword – it’s a driving force behind business innovation. Global enterprises are projected to invest a staggering $307 billion on AI solutions in 2025, fueling a competitive race among solution providers. From tech giants to specialized consultancies, companies worldwide are delivering cutting-edge AI systems that automate processes, uncover insights, and transform customer experiences. Below we rank the Top 7 AI solutions delivery companies of 2025, highlighting their size, focus areas, and how they’re leading the AI revolution. Each company snapshot includes 2024 revenues, workforce size, and core services. 1. Transition Technologies MS (TTMS) Transition Technologies MS (TTMS) is a Poland-headquartered IT services provider that has rapidly emerged as a leader in delivering AI-powered solutions. Operating since 2015, TTMS has grown to over 800 specialists with deep expertise in custom software, cloud, and AI integrations. TTMS stands out for its AI-driven offerings – for example, the company implemented AI to automate complex tender document analysis for a pharma client, significantly improving efficiency in drug development pipelines. As a certified partner of Microsoft, Adobe, and Salesforce, TTMS combines enterprise platforms with AI to build end-to-end solutions tailored to clients’ needs. Its portfolio spans AI solutions for business, from legal document analysis to e-learning and knowledge management, showcasing TTMS’s ability to apply AI across industries. Recent case studies include integrating AI with Salesforce CRM at Takeda for automated bid proposal analysis and deploying an AI tool to summarize court documents for a law firm, underscoring TTMS’s innovative edge in real-world AI implementations. TTMS: company snapshot Revenues in 2024: PLN 233.7 million Number of employees: 800+ Website: https://ttms.com/ai-solutions-for-business/ Headquarters: Warsaw, Poland Main services / focus: AEM, Azure, Power Apps, Salesforce, BI, AI, Webcon, e-learning, Quality Management 2. Amazon Web Services (Amazon) Amazon is not only an e-commerce titan but also a global leader in AI-driven cloud and automation services. Through Amazon Web Services (AWS), Amazon offers a vast suite of AI and machine learning solutions – from pre-trained vision and language APIs to its Bedrock platform hosting foundation models. In 2025, Amazon has integrated AI across its consumer and cloud offerings, launching its own family of AI models (codenamed Nova) for tasks like autonomous web browsing and real-time conversations. Alexa and other Amazon products leverage AI to serve millions of users, and AWS’s AI services enable enterprises to build custom intelligent applications at scale. Backed by enormous scale, Amazon reported $638 billion in revenue in 2024 and employs over 1.5 million people worldwide, making it the largest company on this list by size. With AI embedded deeply in its operations – from warehouse robotics to cloud data centers – Amazon is driving AI adoption globally through powerful infrastructure and continuous innovation in generative AI. Amazon: company snapshot Revenues in 2024: $638.0 billion Number of employees: 1,556,000+ Website: aws.amazon.com Headquarters: Seattle, Washington, USA Main services / focus: Cloud computing (AWS), AI/ML services, e-commerce platforms, voice AI (Alexa), automation 3. Alphabet (Google) Google (Alphabet Inc.) has long been at the forefront of AI research and deployment. In 2025, Google’s expertise in algorithms and massive data processing underpins its Google Cloud AI offerings and consumer products. Google’s cutting-edge Gemini AI ecosystem provides generative AI capabilities on its cloud, enabling developers and businesses to use Google’s models for text, image, and code generation. The company’s AI innovations span from Google Search (with AI-powered answers) to Android and Google Assistant, and its DeepMind division pushes the envelope in areas like reinforcement learning. Google reported roughly $350 billion in revenue for 2024 and about 187,000 employees globally. With initiatives in responsible AI and an array of tools (like Vertex AI, TensorFlow, and generative models), Google helps enterprises integrate AI into products and operations. Whether through Google Cloud’s AI platform or open-source frameworks, Google’s focus is on “AI for everyone” – delivering powerful AI services to both technical and non-technical audiences. Google (Alphabet): company snapshot Revenues in 2024: $350 billion Number of employees: 187,000+ Website: cloud.google.com Headquarters: Mountain View, California, USA Main services / focus: Search & ads, Cloud AI services, generative AI (Gemini, Bard), enterprise apps (Google Workspace), DeepMind research 4. Microsoft Microsoft has positioned itself as an enterprise leader in AI, infusing AI across its product ecosystem. In partnership with OpenAI, Microsoft has integrated GPT-4 and other advanced models into Azure (its cloud platform) and flagship products like Microsoft 365 (introducing AI “Copilot” features in Office apps). The company’s strategy focuses on democratizing AI to boost productivity – for example, empowering users with AI assistants in coding (GitHub Copilot) and writing (Word and Outlook suggestions). Microsoft’s heavy investment in AI infrastructure and supercomputing (including building some of the world’s most powerful AI training clusters for OpenAI) underscores its commitment. In 2024, Microsoft’s revenue topped $245 billion, and it employs about 228,000 people worldwide. Key AI offerings include Azure AI services (cognitive APIs, Azure OpenAI Service), Power Platform AI (low-code AI integration), and industry solutions in healthcare, finance, and retail. With its cloud footprint and software legacy, Microsoft provides robust AI platforms for enterprises, making AI accessible through the tools businesses already use. Microsoft: company snapshot Revenues in 2024: $245 billion Number of employees: 228,000+ Website: azure.microsoft.com Headquarters: Redmond, Washington, USA Main services / focus: Cloud (Azure) and AI services, enterprise software (Microsoft 365, Dynamics), AI-assisted developer tools, OpenAI partnership 5. Accenture Accenture is a global professional services firm renowned for helping businesses implement emerging technologies, and AI is a centerpiece of its offerings. With a workforce of 774,000+ professionals worldwide and revenues around $65 billion in 2024, Accenture has the scale and expertise to deliver AI solutions across all industries – from finance and healthcare to retail and manufacturing. Accenture’s dedicated Applied Intelligence practice offers end-to-end AI services: strategy consulting, data engineering, custom model development, and system integration. The firm has developed industry-tailored AI platforms (for example, its ai.RETAIL platform that uses AI for real-time merchandising and predictive analytics in retail) and invested heavily in AI talent and acquisitions. Accenture distinguishes itself by integrating AI with business process knowledge – using automation, analytics, and AI to reinvent clients’ operations at scale. As organizations navigate generative AI and automation, Accenture provides guidance on responsible AI adoption and even retrains its own employees in AI skills to meet demand. Headquartered in Dublin, Ireland, with offices in over 120 countries, Accenture leverages its global reach to roll out AI innovations and best practices for enterprises worldwide. Accenture: company snapshot Revenues in 2024: ~$65 billion Number of employees: 774,000+ Website: accenture.com Headquarters: Dublin, Ireland Main services / focus: AI consulting & integration, analytics, cloud services, digital transformation, industry-specific AI solutions 6. IBM IBM has been a pioneer in AI since the early days – from chess-playing computers to today’s enterprise AI solutions. In 2025, IBM’s AI portfolio is headlined by the Watson platform and the new watsonx AI development studio, which offer businesses tools for building AI models, automating workflows, and deploying conversational AI. IBM, headquartered in Armonk, New York, generated about $62.7 billion in 2024 revenue and has approximately 270,000 employees globally. Known as “Big Blue,” IBM focuses on AI for hybrid cloud and enterprise automation – helping clients integrate AI into everything from customer service (chatbots) to IT operations (AIOps) and risk management. Its research heritage (IBM Research) and accumulation of patents ensure a steady infusion of advanced AI techniques into products. IBM’s strengths lie in conversational AI, machine learning, and AI-powered automation, often targeting industry-specific needs (like AI in healthcare diagnostics or financial fraud detection). With decades of trust from large enterprises, IBM often serves as a strategic AI partner that can handle sensitive data and complex integration, bolstered by its investments in AI ethics and partnerships with academia. From mainframes to modern AI, IBM continues to reinvent its offerings to stay at the cutting edge of intelligent technology. IBM: company snapshot Revenues in 2024: $62.8 billion Number of employees: 270,000+ Website: ibm.com Headquarters: Armonk, New York, USA Main services / focus: Enterprise AI (Watson/watsonx), hybrid cloud, AI-powered consulting, IT automation, data analytics 7. Tata Consultancy Services (TCS) Tata Consultancy Services (TCS) is one of the world’s largest IT services and consulting companies, known for its vast global delivery network and expertise in digital transformation. Part of India’s Tata Group, TCS had $29-30 billion in revenue in 2024 and a massive talent pool of over 600,000 employees. TCS offers a broad spectrum of services with a growing emphasis on AI, analytics, and automation solutions. The company works with clients worldwide to develop AI applications such as predictive maintenance systems for manufacturing, AI-driven customer personalization in retail, and intelligent process automation in banking. Leveraging its scale, TCS has built frameworks and accelerators (like TCS AI Workbench and Ignio, its cognitive automation software) to speed up AI adoption for enterprises. Headquartered in Mumbai, India, and operating in 46+ countries, TCS combines deep domain knowledge with tech expertise. Its focus on AI and machine learning is part of a broader strategy to help businesses become “cognitive enterprises” – using AI to enhance decision-making, optimize operations, and create new value. With strong execution capabilities and R&D (TCS Research labs), TCS is a go-to partner for many Fortune 500 firms embarking on AI-led transformations. TCS: company snapshot Revenues in 2024: $30 billion Number of employees: 600,000+ Website: tcs.com Headquarters: Mumbai, India Main services / focus: IT consulting & services, AI & automation solutions, enterprise software development, business process outsourcing, analytics Why Choose TTMS for AI Solutions? When it comes to implementing AI initiatives, TTMS (Transition Technologies MS) offers the agility and innovation of a focused specialist backed by a track record of success. TTMS combines deep technical expertise with personalized service, making it an ideal partner for organizations looking to harness AI effectively. Unlike industry giants that might take a one-size-fits-all approach, TTMS delivers bespoke AI solutions tailored to each client’s unique needs – ensuring faster deployment and closer alignment with business goals. The company’s experience across diverse sectors (from legal to pharma) and its roster of skilled AI engineers enable TTMS to tackle projects of any complexity. As a testament to its capabilities, here are a few TTMS AI success stories that demonstrate how TTMS drives tangible results: AI Implementation for Court Document Analysis at a Law Firm: TTMS developed an AI solution for a legal client (Sawaryn & Partners) that automates the analysis of court documents and transcripts, massively reducing manual workload. By leveraging Azure OpenAI services, the system can generate summaries of case files and hearing recordings, enabling lawyers to find key information in seconds. This project improved the law firm’s efficiency and data security, as large volumes of sensitive documents are processed internally with AI – speeding up case preparations while maintaining confidentiality. AI-Driven SEO Meta Optimization: For Stäubli, a global industrial manufacturer, TTMS implemented an AI solution to optimize SEO metadata across thousands of product pages. Integrated with Adobe Experience Manager, the system uses ChatGPT to automatically generate SEO-friendly page titles and meta descriptions based on page content. Content authors can then review and fine-tune these AI-suggested titles. This approach saved significant time for Stäubli’s team and boosted the website’s search visibility by ensuring consistent, keyword-optimized metadata on every page. Enhancing Helpdesk Training with AI: TTMS created an AI-powered e-learning platform to train a client’s new helpdesk employees in responding to support tickets. The solution presents trainees with simulated customer inquiries and uses AI to provide real-time feedback on their draft responses. By interacting with the AI tutor, new hires quickly learn to write replies that adhere to company guidelines and improve their English communication skills. This resulted in faster onboarding, more consistent customer service, and higher confidence among support staff in handling tickets. Salesforce Integration with an AI Tool: TTMS built a custom AI integration for Takeda Pharmaceuticals, embedding AI into the company’s Salesforce CRM system to streamline the complex process of managing drug tender offers. The solution automatically analyzes incoming requests for proposals (RFPs) – extracting key requirements, deadlines, and criteria – and provides preliminary bid assessments to assist decision-makers. By combining Salesforce data with AI-driven analysis, Takeda’s team can respond to tenders more quickly and accurately. This innovation saved the company substantial time and improved the quality of its bids in a highly competitive, regulated industry. Beyond these projects, TTMS has developed a suite of proprietary AI tools that demonstrate its forward-thinking approach. These in-house solutions address common business challenges with specialized AI applications: AI4Legal: A legal-tech toolset that uses AI to assist with contract drafting, review, and risk analysis, allowing law firms and legal departments to automate document analysis and ensure compliance. AML Track: An AI-powered AML system designed to detect suspicious activities and support financial compliance, helping institutions identify fraud and meet regulatory requirements with precision and speed. AI4Localisation: Intelligent localization services that leverage AI to translate and adapt content across languages while preserving cultural nuance and tone consistency, streamlining global marketing and documentation. AI-Based Knowledge Management System: A smart knowledge base platform that organizes corporate information and FAQs, using AI to enable faster information retrieval and smarter search through company data silos. AI E-Learning: A tool for creating AI-driven training modules that adapt to learners’ needs, allowing organizations to build interactive e-learning content at scale with personalized learning paths. AI4Content: An AI solution for documents that can automatically extract, validate, and summarize information from large volumes of text (such as forms, reports, or contracts), drastically reducing manual data entry and review time. Choosing TTMS means partnering with a provider that stays on the cutting edge of AI trends while maintaining a client-centric approach. Whether you need to implement a machine learning model, integrate AI into enterprise software, or develop a custom intelligent tool, TTMS has the experience, proprietary technology, and dedication to ensure your AI project succeeds. Harness the power of AI for your business with TTMS – your trusted AI solutions delivery partner. Contact us! FAQ What is an “AI solutions delivery” company? An AI solutions delivery company is a service provider that designs, develops, and implements artificial intelligence systems for clients. These companies typically have expertise in technologies like machine learning, data analytics, natural language processing, and automation. They work with businesses to identify opportunities where AI can add value (such as automating a process or gaining insights from data) and then build custom AI-powered applications or integrate third-party AI tools. In essence, an AI solutions provider takes cutting-edge AI research and applies it to real-world business challenges – delivering tangible solutions like predictive models, chatbots, computer vision systems, or intelligent workflow automations. How do I choose the best AI solutions provider for my business? Selecting the right AI partner involves evaluating a few key factors. First, consider the company’s experience and domain expertise – do they have a track record of projects in your industry or addressing similar problems? Review their case studies and client testimonials for evidence of successful outcomes. Second, assess their technical capabilities: a good provider should have skilled data scientists, engineers, and consultants who understand both cutting-edge AI techniques and how to deploy them at scale. It’s also wise to look at their partnerships (for instance, are they partners with major cloud AI platforms like AWS, Google Cloud, or Azure?) as this can expand the solutions they offer. Finally, ensure their approach aligns with your needs – the best providers will take time to understand your business objectives and customize an AI solution (rather than forcing a one-size-fits-all product). Comparing proposals and conducting pilot projects can further help in choosing a provider that delivers both expertise and a comfortable working relationship. What AI services does TTMS provide? Transition Technologies MS (TTMS) offers a broad range of AI services, tailored to help organizations deploy AI effectively. TTMS can engage end-to-end in your AI project: from initial consulting and strategy (identifying use cases and assessing data readiness) to solution development and integration. Concretely, TTMS builds custom AI applications (for example, predictive analytics models, NLP solutions for document analysis, or computer vision systems) and also integrates AI into existing platforms like CRM systems or content management systems. The company provides data engineering and preparation, ensuring your data is ready for AI modeling, and employs machine learning techniques to create intelligent features (like recommendation engines or anomaly detectors) for your software. Additionally, TTMS offers specialized solutions such as AI-driven automation of business processes, AI in cybersecurity (fraud detection, AML systems), AI for content generation/optimization (as seen in their SEO meta optimization case), and much more. With its team of AI experts, TTMS essentially can take any complex manual process or decision-making workflow and find a way to enhance it with artificial intelligence. Why are companies like Amazon, Google, and IBM leaders in AI solutions? Tech giants such as Amazon, Google, Microsoft, IBM, etc., have risen to prominence in AI for several reasons. Firstly, they have invested heavily in research and development – these companies employ leading AI scientists and have contributed fundamental advancements (for instance, Google’s deep learning research via DeepMind or OpenAI partnership with Microsoft). This R&D prowess means they often have cutting-edge AI technology (like Google’s state-of-the-art language models or IBM’s Watson platform) ready to deploy. Secondly, they possess massive computing infrastructure and data. AI development, especially training large models, requires huge computational resources and large datasets – something these tech giants have in abundance through their cloud divisions and user bases. Thirdly, they have integrated AI into a broad array of services and made them accessible: Amazon’s AWS offers AI building blocks for developers, Google Cloud does similarly, and Microsoft embeds AI features into tools that businesses already use. Lastly, their global scale and enterprise experience give them credibility; they have proven solutions in many domains (from Amazon’s AI-driven logistics to IBM’s enterprise AI consulting) which showcases reliability. In summary, these companies lead in AI solutions because they combine innovation, infrastructure, and industry know-how to deliver AI capabilities worldwide. Can smaller companies like TTMS compete with global IT giants in AI? Yes, smaller specialized firms like TTMS can absolutely compete and often provide unique advantages over the mega-corporations. While they may not match the sheer size or brand recognition of a Google or IBM, companies like TTMS are typically more agile and focused. They can adapt quickly to the latest AI developments and often tailor their services more closely to individual client needs (large firms might push more standardized solutions or have more bureaucracy). TTMS, for instance, zeroes in on client-specific AI solutions – meaning they will develop a custom model or tool specifically for your problem, rather than a generic platform. Additionally, specialized providers tend to offer more personalized attention; clients work directly with senior engineers or AI experts, ensuring in-depth understanding of the project. There’s also the fact that AI talent is distributed – smaller companies often attract top experts who prefer a focused environment. That said, big players do bring strengths like vast resources and pre-built platforms, but smaller AI firms compete by being innovative, customer-centric, and flexible on cost and project scope. In practice, many enterprises employ a mix: using big cloud AI services under the guidance of a nimble partner like TTMS to get the best of both worlds.
Read
Deepfake Detection Breakthrough: Universal Detector Achieves 98% Accuracy
Deepfake Detection Breakthrough: Universal Detector Achieves 98% Accuracy Imagine waking up to a viral video of your company’s CEO making outrageous claims – except it never happened. This nightmare scenario is becoming all too real as deepfakes (AI-generated fake videos or audio) grow more convincing. In response, researchers have unveiled a new universal deepfake detector that can spot synthetic videos with an unprecedented 98% accuracy. The development couldn’t be more timely, as businesses seek ways to protect their brand reputation and trust in an era when seeing is no longer believing. A powerful new AI tool can analyze videos and detect subtle signs of manipulation, helping companies distinguish real footage from deepfakes. The latest “universal” detector boasts cross-platform capabilities, flagging both fake videos and AI-generated audio with remarkable precision. It marks a significant advance in the fight against AI-driven disinformation. What is the 98% Accurate Universal Deepfake Detector and How Does It Work? The newly announced deepfake detector is an AI-driven system designed to identify fake video and audio content across virtually any platform. Developed by a team of researchers (notably at UC San Diego in August 2025), it represents a major leap forward in deepfake detection technology. Unlike earlier tools that were limited to specific deepfake formats, this “universal” detector works on both AI-generated speech and manipulated video footage. In other words, it can catch a lip-synced synthetic video of an executive and an impersonated voice recording with the same solution. Under the hood, the detector uses advanced machine learning techniques to sniff out the subtle “fingerprints” that generative AI leaves on fake content. When an image or video is created by AI rather than a real camera, there are tiny irregularities at the pixel level and in motion patterns that human eyes can’t easily see. The detector’s neural network has been trained to recognize these anomalies at the sub-pixel scale. For example, real videos have natural color correlations and noise characteristics from camera sensors, whereas AI-generated frames might have telltale inconsistencies in texture or lighting. By focusing on these hidden markers, the system can discern AI fakery without relying on obvious errors. Critically, this new detector doesn’t just focus on faces or one part of the frame – it scans the entire scene (backgrounds, movements, audio waveform, etc.) for anything that “doesn’t fit.” Earlier deepfake detectors often zeroed in on facial glitches (like unnatural eye blinking or odd skin textures) and could fail if no face was visible. In contrast, the universal model analyzes multiple regions per frame and across consecutive frames, catching subtle spatial and temporal inconsistencies that older methods missed. It’s a transformer-based AI model that essentially learns what real vs. fake looks like in a broad sense, instead of using one narrow trick. This breadth is what makes it universal – as one researcher put it, “It’s one model that handles all these scenarios… that’s what makes it universal”. Training Data and Testing: Building a Better Fake-Spotter Achieving 98% accuracy required feeding the detector a huge diet of both real and fake media. The researchers trained the system on an extensive range of AI-generated videos produced by different generator programs – from deepfake face-swaps to fully AI-created clips. For instance, they used samples from tools like Stable Diffusion’s video generator, Video-Crafter, and CogVideo to teach the AI what various fake “fingerprints” look like. By learning from many techniques, the model doesn’t get fooled by just one type of deepfake. Impressively, the team reported that the detector can even adapt to new deepfake methods after seeing only a few examples. This means if a brand-new AI video generator comes out next month, the detector could learn its telltale signs without needing a complete retraining. The results of testing this system have been record-breaking. In evaluations, the detector correctly flagged AI-generated videos about 98.3% of the time. This is a significant jump in accuracy compared to prior detection tools, which often struggled to get above the low 90s. In fact, the researchers benchmarked their model against eight different existing deepfake detection systems, and the new model outperformed all of them (the others ranged around 93% accuracy or lower). Such a high true-positive rate is a major milestone in the arms race against deepfakes. It suggests the AI can spot almost all fake content thrown at it, across a wide variety of sources. Of course, “98% accuracy” isn’t 100%, and that remaining 2% error rate does matter. With millions of videos uploaded online daily, even a small false-negative rate means some deepfakes will slip through, and a false-positive rate could flag some real videos incorrectly. Nonetheless, this detector’s performance is currently best-in-class. It gives organizations a fighting chance to catch malicious fakes that would have passed undetected just a year or two ago. As deepfake generation gets more advanced, detection had to step up – and this tool shows it’s possible to significantly close the gap. How Is This Detector Different from Past Deepfake Detection Methods? Previous deepfake detection methods were often specialized and easier to evade. One key difference is the new detector’s broad scope. Earlier detectors typically focused on specific artifacts – for example, one system might look for unnatural facial movements, while another analyzed lighting mismatches on a person’s face. These worked for certain deepfakes but failed for others. Many classic detectors also treated video simply as a series of individual images, trying to spot signs of Photoshop-style edits frame by frame. That approach falls apart when dealing with fully AI-generated video, which doesn’t have obvious cut-and-paste traces between frames. By contrast, the 98% accurate detector looks at the bigger picture (pun intended): it examines patterns over time and across the whole frame, not just isolated stills. Another major advancement is the detector’s ability to handle various formats and even modalities. Past solutions usually targeted one type of media at a time – for instance, a tool might detect face-swap video deepfakes but do nothing about an AI-cloned voice in an audio clip. The new universal detector can tackle both video and audio in one system, which is a game-changer. So if a deepfake involves a fake voice over a real video, or vice versa, older detectors might miss it, whereas this one catches the deception in either stream. Additionally, the architecture of this detector is more sophisticated. It employs a constrained neural network that homes in on anomalies in data distributions rather than searching for a predefined list of errors. Think of older methods like using a checklist (“Are the eyes blinking normally? Is the heartbeat visible on the neck?”) – effective until the deepfake creators fix those specific issues. The new method is more like an all-purpose lie detector for media; it learns the underlying differences between real and fake content, which are harder for forgers to eliminate. Also, unlike many legacy detectors that heavily relied on seeing a human face, this model doesn’t care if the content has people, objects, or scenery. For example, if someone fabricated a video of an empty office with fake background details, previous detectors might not notice anything since no face is present. The universal detector would still scrutinize the textures, shadows, and motion in the scene for unnatural signs. This makes it resilient against a broader array of deepfake styles. In summary, what sets this new detector apart is its universality and robustness. It’s essentially a single system that covers many bases: face swaps, entirely synthetic videos, fake voices, and more. Earlier generations of detectors were more narrow – they solved part of the problem. This one combines lessons from all those earlier efforts into a comprehensive tool. That breadth is vital because deepfake threats are evolving too. By solving the cross-platform compatibility issues that plagued older systems, the detector can maintain high accuracy even as deepfake techniques diversify. It’s the difference between a patchwork of local smoke detectors and a building-wide fire alarm system. Why This Matters for Brand Safety and Reputational Risk For businesses, deepfakes aren’t just an IT problem – they’re a serious brand safety and reputation risk. We live in a time where a single doctored video can go viral and wreak havoc on a company’s credibility. Imagine a fake video showing your CEO making unethical remarks or a bogus announcement of a product recall; such a hoax could send stock prices tumbling and customers fleeing before the truth gets out. Unfortunately, these scenarios have moved from hypothetical to real. Corporate targets are already in the crosshairs of deepfake fraudsters. In 2019, for example, criminals used an AI voice clone to impersonate a CEO and convinced an employee to wire $243,000 to a fraudulent account. By 2024, a multinational firm in Hong Kong was duped by an even more elaborate deepfake – a video call with a fake “CEO” and colleagues – resulting in a $25 million loss. The number of deepfake attacks against companies has surged, with AI-generated voices and videos duping financial firms out of millions and putting corporate security teams on high alert. Beyond direct financial theft, deepfakes pose a huge reputational threat. Brands spend years building trust, which a single viral deepfake can undermine in minutes. There have been cases of fake videos of political leaders and CEOs circulating online – even if debunked eventually, the damage in the interim can be significant. Consumers might question, “Was that real?” about any shocking video involving your brand. This uncertainty erodes the baseline of trust that businesses rely on. That’s why a detection tool with very high accuracy matters: it gives companies a fighting chance to identify and respond to fraudulent media quickly, before rumors and misinformation take on a life of their own. From a brand safety perspective, having a nearly foolproof deepfake detector is like having an early-warning radar for your reputation. It can help verify the authenticity of any suspicious video or audio featuring your executives, products, or partners. For example, if a doctored video of your CEO appears on social media, the detector could flag it within moments, allowing your team to alert the platform and your audience that it’s fake. Consider how valuable that is – it could be the difference between a contained incident and a full-blown PR crisis. In industries like finance, news media, and consumer goods, where public confidence is paramount, such rapid detection is a lifeline. As one industry report noted, this kind of tool is a “lifeline for companies concerned about brand reputation, misinformation, and digital trust”. It’s becoming essential for any organization that could be a victim of synthetic content abuse. Deepfakes have also introduced new vectors for fraud and misinformation that traditional security measures weren’t prepared for. Fake audio messages of a CEO asking an employee to transfer money, or a deepfake video of a company spokesperson giving false information about a merger, can bypass many people’s intuitions because we are wired to trust what we see and hear. Brand impersonation through deepfakes can mislead customers – for instance, a fake video “announcement” could trick people into a scam investment or phishing scheme using the company’s good name. The 98% accuracy detector, deployed properly, acts as a safeguard against these malicious uses. It won’t stop deepfakes from being made (just as security cameras don’t stop crimes by themselves), but it significantly boosts the chance of catching a fake in time to mitigate the harm. Incorporating Deepfake Detection into Business AI and Cybersecurity Strategies Given the stakes, businesses should proactively integrate deepfake detection tools into their overall security and risk management framework. A detector is not just a novelty for the IT department; it’s quickly becoming as vital as spam filters or antivirus software in the corporate world. Here are some strategic steps and considerations for companies looking to defend against deepfake threats: Employee Education and Policies: Train staff at all levels to be aware of deepfake scams and to verify sensitive communications. For example, employees should be skeptical of any urgent voice message or video that seems even slightly off. They must double-check unusual requests (especially involving money or confidential data) through secondary channels (like calling back a known number). Make it company policy that no major action is taken based on electronic communications alone without verification. Strengthen Verification Processes: Build robust verification protocols for financial transactions and executive communications. This might include multi-factor authentication for approvals, code words for confirming identity, or mandatory pause-and-verify steps for any request that seems odd. An incident in 2019 already highlighted that recognizing a voice is no longer enough to confirm someone’s identity – so treat video and audio with the same caution as you would a suspicious email. Deploy AI-Powered Detection Tools: Incorporate deepfake detection technology into your cybersecurity arsenal. Specialized software or services can analyze incoming content (emails with video attachments, voicemail recordings, social media videos about your brand) and flag possible fakes. Advanced AI detection systems can catch subtle inconsistencies in audio and video that humans would miss. Many tech and security firms are now offering detection as a service, and some social media platforms are building it into their moderation processes. Use these tools to automatically screen content – like an “anti-virus” for deepfakes – so you get alerts in real time. Regular Drills and Preparedness: Update your incident response plan to include deepfake scenarios. Conduct simulations (like a fake “CEO video” emergency drill) to test how your team would react. Just as companies run phishing simulations, run a deepfake drill to ensure your communications, PR, and security teams know the protocol if a fake video surfaces. This might involve quickly assembling a crisis team, notifying platform providers to take down the content, and issuing public statements. Practicing these steps can greatly reduce reaction time under real pressure. Monitor and Respond in Real Time: Assign personnel or use services to continuously monitor for mentions of your brand and key executives online. If a deepfake targeting your company does appear, swift action is crucial. The faster you identify it’s fake (with the help of detection AI) and respond publicly, the better you can contain false narratives. Have a clear response playbook: who assesses the content, who contacts legal and law enforcement if needed, and who communicates to the public. Being prepared can turn a potential nightmare into a managed incident. Integrating these measures ensures that your deepfake defense is both technical and human. No single tool is a silver bullet – even a 98% accurate detector works best in concert with good practices. Companies that have embraced these strategies treat deepfake risk as a when-not-if issue. They are actively “baking deepfake detection into their security and compliance practices,” as analysts advise. By doing so, businesses not only protect themselves from fraud and reputational damage but also bolster stakeholder confidence. In a world where AI can imitate anyone, a robust verification and detection strategy becomes a cornerstone of digital trust. Looking ahead, we can expect deepfake detectors to be increasingly common in enterprise security stacks. Just as spam filters and anti-malware became standard, content authentication and deepfake scanning will likely become routine. Forward-thinking companies are already exploring partnerships with AI firms to integrate detection APIs into their video conferencing and email systems. The investment in these tools is far cheaper than the cost of a major deepfake debacle. With threats evolving, businesses must stay one step ahead – and this 98% accuracy detector is a promising tool to help them do exactly that. Protect Your Business with TTMS AI Solutions At Transition Technologies MS (TTMS), we help organizations strengthen their defenses against digital threats by integrating cutting-edge AI tools into cybersecurity strategies. From advanced document analysis to knowledge management and e-learning systems, our AI-driven solutions are designed to ensure trust, compliance, and resilience in the digital age. Partner with TTMS to safeguard your brand reputation and prepare for the next generation of challenges in deepfake detection and beyond. FAQ How can you tell if a video is a deepfake without specialized tools? Even without an AI detector, there are some red flags that a video might be a deepfake. Look closely at the person’s face and movements – often, early deepfakes had unnatural eye blinking or facial expressions that seem “off.” Check for inconsistencies in lighting and shadows; sometimes the subject’s face lighting won’t perfectly match the scene. Audio can be a giveaway too: mismatched lip-sync or robotic-sounding voices might indicate manipulation. Pause on individual frames if possible – distorted or blurry details around the edges of faces (especially between transitions) can signal something is amiss. While these clues can help, sophisticated deepfakes today are much harder to spot with the naked eye, which is why tools and detectors are increasingly important. Are there laws or regulations addressing deepfakes that companies should know about? Regulation of deepfakes is starting to catch up as the technology’s impact grows. Different jurisdictions have begun introducing laws to deter malicious use of deepfakes. For example, China implemented regulations requiring that AI-generated media (deepfakes) be clearly labeled, and it bans the creation of deepfakes that could mislead the public or harm someone’s reputation. In the European Union, the upcoming AI Act treats manipulative AI content as high-risk and will likely enforce transparency obligations – meaning companies may need to disclose AI-generated content and could face penalties for harmful deepfake misuse. In the United States, there isn’t a blanket federal deepfake law yet, but some states have acted: Virginia was one of the first, criminalizing certain deepfake pornography and impersonations, and California and Texas have laws against deepfakes in elections. Additionally, existing laws on fraud, defamation, and identity theft can apply to deepfake scenarios (for instance, using a deepfake to commit fraud is still fraud). For businesses, this regulatory landscape means two things: you should refrain from unethical uses of deepfakes in your operations and marketing (to avoid legal trouble and backlash), and you should stay informed about emerging laws that protect victims of deepfakes – such laws might aid your company if you ever need to take legal action against parties making malicious fakes. It’s wise to consult legal experts on how deepfake-related regulations in your region could affect your compliance and response strategies. Can deepfake creators still fool a 98% accurate detector? It’s difficult but not impossible. A 98% accurate detector is extremely good, but determined adversaries are always looking for ways to evade detection. Researchers have shown that by adding specially crafted “noise” or artifacts (called adversarial examples) into a deepfake, they can sometimes trick detection models. It’s an AI cat-and-mouse game: as detectors improve, deepfake techniques adjust to become more sneaky. That said, fooling a top-tier detector requires a lot of expertise and effort – the average deepfake circulating online right now is unlikely to be that expertly concealed. The new universal detector raises the bar significantly, meaning most fakes out there will be caught. But we can expect deepfake creators to try developing countermeasures, so ongoing research and updated models will be needed. In short, 98% accurate doesn’t mean invincible, but it makes successful deepfake attacks much rarer. What should a company do if a deepfake of its CEO or brand goes public? Facing a deepfake attack on your company requires swift and careful action. First, internally verify the content – use detection tools (like the 98% accuracy detector) to confirm it’s fake, and gather any evidence of how it was created if possible. Activate your crisis response team immediately; this typically involves corporate communications, IT security, legal counsel, and executive leadership. Contact the platform where the video or audio is circulating and report it as fraudulent content – many social networks and websites have policies against deepfakes, especially those causing harm, and will remove them when alerted. Simultaneously, prepare a public statement or press release for your stakeholders. Be transparent and assertive: inform everyone that the video/audio is a fake and that malicious actors are attempting to mislead the public. If the deepfake could have legal ramifications (for example, stock manipulation or defamation), involve law enforcement or regulators as needed. Going forward, conduct a post-incident analysis to improve your response plan. By reacting quickly and communicating clearly, a company can often turn the tide and prevent lasting damage from a deepfake incident. Are deepfake detection tools available for businesses to use? Yes – while some cutting-edge detectors are still in the research phase, there are already tools on the market that businesses can leverage. A number of cybersecurity companies and AI startups offer deepfake detection services (often integrated into broader threat intelligence platforms). For instance, some provide APIs or software that can scan videos and audio for signs of manipulation. Big tech firms are also investing in this area; platforms like Facebook and YouTube have developed internal deepfake detection to police their content, and Microsoft released a deepfake detection tool (Video Authenticator) a few years ago. Moreover, open-source projects and academic labs have published deepfake detection models that savvy companies can experiment with. The new 98% accuracy “universal” detector itself may become commercially or publicly available after further development – if so, it could be deployed by businesses much like antivirus software. It’s worth noting that effective use of these tools also requires human oversight. Businesses should assign trained staff or partner with vendors to implement the detectors correctly and interpret the alerts. In summary, while no off-the-shelf solution is perfect, a variety of deepfake detection options do exist and are maturing rapidly.
ReadThe world’s largest corporations have trusted us
We hereby declare that Transition Technologies MS provides IT services on time, with high quality and in accordance with the signed agreement. We recommend TTMS as a trustworthy and reliable provider of Salesforce IT services.
TTMS has really helped us thorough the years in the field of configuration and management of protection relays with the use of various technologies. I do confirm, that the services provided by TTMS are implemented in a timely manner, in accordance with the agreement and duly.
Ready to take your business to the next level?
Let’s talk about how TTMS can help.
Michael Foote
Business Leader & CO – TTMS UK