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AI Software Factory: Controlled Agentic Development for Enterprise Teams

An AI software factory is a structured way to introduce AI agents into an enterprise software development process. We configure their work within the client’s repositories and CI/CD environment, establish quality controls, and prepare the engineering team to use the new delivery model safely.

We work in line with internationally recognized standards.

ISO/IEC 42001

AI MANAGEMENT SYSTEM

ISO/IEC 27001

INFORMATION SECURITY

ISO 9001

QUALITY MANAGEMENT

ISO/IEC 20000

IT SERVICE MANAGEMENT

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What Our Clients Say

Mateusz Sawaryn

Kancelaria Prawna „Sawaryn i Partnerzy”

We are fully satisfied with our cooperation with TTMS and recommend them as a reliable technology partner, experienced in technologies based on artificial intelligence.

Soenke Kock

Division R&D Manager and Head of Development IoT & Analytics Motion Services Division ABB Business Area Motion

As part of our cooperation with TTMS, we are currently running research projects (POC) analyzing the usefulness of AI-based tools (including GitHub Copilot) in the software development process.

TAKEDA

TTMS implemented a solution supporting the work of the department that analyzes public tenders. The Salesforce-based system, supported by AI algorithms, helps optimize and automate document analysis.

AI Case studies by TTMS

Transforming Biopharmaceutical Tender Processes: AI Integration and Automation at Takeda Pharma

INDUSTRY:
Global biopharmaceutical company, Takeda Pharma, specializing in digestive system diseases, oncology, neurological diseases, and plasma-based therapies.
CHALLENGE:
The Polish branch struggled with complex and lengthy tender processes, difficult documentation, and poor data protection.
SOLUTION:
TTMS introduced an AI system integrated with Salesforce CRM to automatically analyze and assess key criteria from RFPs.
RESULT FOR CLIENT:
The AI solution improved the bidding process, enabling quicker, more accurate decision-making and better resource allocation.

Optimizing Sales Efficiency at leading automotive company: AI-Driven Discount Determination System

INDUSTRY:
Automotive. The manufacturer needed to optimize the sales process by automating discount calculations for different car models.
CHALLENGE:
Calculating discounts involved multiple complex factors such as model year, engine type, and additional services.
SOLUTION:
A PoC was developed using Azure AI and GPT-4 to automatically process vehicle parameter queries and determine discounts.
RESULT FOR CLIENT:
The AI-driven system allowed a global car manufacturer to efficiently manage data, providing faster and more precise discount calculations.

Enhancing Operational Efficiency with AI: The leading sweets polish producer – ChatBot Integration Case Study

INDUSTRY:
Confectionery. Our client sought to improve operational efficiency through AI-driven solutions.
CHALLENGE:
Employees had difficulty accessing and managing HR and administrative information stored in various formats on their SharePoint intranet.
SOLUTION:
A Microsoft Teams integrated ChatBot was developed, leveraging AI to quickly access HR and administrative information from SharePoint. It was done using OpenAI services on Microsoft Azure.
RESULT FOR CLIENT:
The ChatBot significantly improved internal query handling, reducing time spent searching for information and boosting productivity.

Enhancing Employees satisfaction with AI: The leading Polish merchant acquirer in Poland – ChatBot for Help Desk Case Study

INDUSTRY:
Finance. Our client sought to improve back-office efficiency through AI-driven solutions.
CHALLENGE:
Employees had difficulty accessing and implementing IT related instructions and procedures stored in various formats on their SharePoint intranet.
SOLUTION:
A Microsoft Teams integrated ChatBot was developed, leveraging AI to quickly access solutions and procedures from SharePoint. It was done using OpenAI services on Microsoft Azure. Employees have an opportunity to chat with digital assistant directly from Teams chat and engage real person if the need more sophisticated help.
RESULT FOR CLIENT:
The ChatBot significantly improved internal query handling, reducing time spent searching for information and boosting productivity.

Helpdesk Training with AI-Powered
e-Learning Solutions

INDUSTRY:
The company operates a helpdesk service, addressing and resolving customer issues to ensure a seamless and satisfactory customer experience.
CHALLENGE:
The challenge was to create an efficient training program that swiftly onboards new helpdesk employees, ensuring they proficiently handle tickets and address knowledge gaps.
SOLUTION:
The company implemented an AI-powered e-Learning training program, featuring traditional modules and interactive exercises, to help new helpdesk employees handle tickets according to guidelines, improve their writing and English skills, and provide management with insights on common errors.
RESULT FOR CLIENT:
The implementation of the AI-powered e-Learning solution resulted in faster proficiency in handling tickets, more consistent adherence to company guidelines, improved writing and English skills, continuous refinement of the training process, and valuable insights for management.

Optimizing Efficiency in a Leading Law Firm: AI-Based Support System

INDUSTRY:
Legal sector. Law Firm Marek Małecki and Partners needed to optimize their workflow by automating document analysis.
CHALLENGE:
Analysing court transcripts, preparing mediation proposals, document analysis, and contract generation were time-consuming and prone to human error.
SOLUTION:
TTMS implemented a system based on ChatGPT and Azure cloud, automating the analysis of legal documents, mediation preparation, and contract generation, ensuring secure processing of anonymized data.
RESULT FOR CLIENT:
The AI system significantly improved the firm’s workflow, reducing document processing time and increasing analysis accuracy. This allowed lawyers to focus on more complex tasks, enhancing service quality and client satisfaction.

Layer

Transforming HR Support with an AI Knowledge Assistant

INDUSTRY:
A global technology company specializing in providing outsourcing services.
CHALLENGE:
The HR department was burdened with regular questions about benefits, procedures and other employee matters.
SOLUTION:
We created an internal knowledge base for employees (handbook), launched a language model (based on Chat GPT 4.0) that answered questions related to the above handbook.
RESULT FOR CLIENT:
The AI solution relieved the HR department and streamlined the process of obtaining information by employees.

What Is an AI Software Factory?

What it is

An AI Software Factory is a structured operating model that introduces AI agents into an enterprise organization’s existing software development process. TTMS helps configure their work within the client’s repositories and CI/CD environment, define quality controls, and prepare the engineering team to use the new approach safely.

How it differs from an AI coding assistant

AI coding assistantSupports an individual developer.
AI Software FactoryIntegrates AI agents into the full process, from a backlog item to a software release.

Who it is for

The service is designed for organizations with in-house development teams facing:

  • Growing backlogs

  • Delayed legacy modernization

  • Limited delivery improvements despite using AI coding assistants
  • Explore AI Software Factory

    Why Faster Coding Alone
    Does Not Fix Delivery

    Faster coding alone is not enough if testing, reviews, and change approvals continue to delay deployment. AI agents deliver the greatest value when they are part of a structured process covering the entire software development lifecycle.

    Uncontrolled Code Is Security Debt

    AI-generated code may contain vulnerabilities or fail to meet the organization’s standards. Every change should therefore undergo review, testing, and security scanning before deployment.

    No Traceability of Changes

    Without a record of which changes the agent made, which requirement prompted them, and who approved them, identifying the source of an error or providing evidence during an audit becomes difficult.

    Isolated Agents Instead of a Process

    AI coding assistants can accelerate the work of individual developers, but they cannot shorten the path from backlog to production on their own. Teams need a shared process covering testing, quality controls, and clearly defined change approval rules.

    How Our Agentic AI Software Development Services Transform the Path from Backlog to Production

    We configure a controlled process that introduces AI agents into the client team’s workflow, from a backlog item to production deployment. The process operates within the organization’s repositories and CI/CD environment and follows its security, quality, and change approval policies.


  • 01

    Requirement

    The client team selects a backlog item and defines the expected outcome and acceptance criteria. Based on this information, the AI agent prepares a plan of action.


  • 03

    Quality Gates

    Changes pass the tests and controls required by the organization, including Static Application Security Testing (SAST), Software Composition Analysis (SCA), and sensitive data scanning.


  • 05

    Release

    Approved changes follow the client’s established release process. Information about the version, control results, approvals, and software components can be recorded in the audit trail.


  • 02

    AI Agents Write and Test Code

    The AI agent prepares code and tests in an isolated environment, without direct access to production. The proposed changes are submitted to a working branch for further verification.


  • 04

    Human Approval

    An authorized engineer on the client’s side reviews the pull request, checks the changes against the requirements, and examines the test results. The code can be merged only after the required approval.


  • Autonomous Where It Should Be, Human-Controlled Where It Matters

    We define the scope of independent AI agent activity based on risk and the ability to verify the results. Key decisions concerning architecture, merging changes, and production deployment remain with authorized engineers on the client’s side.

    Who Makes the Decision at Each Stage?

    Area
    What can the AI agent do independently?
    Who approves the result?
    Code and unit test generation
    The agent can prepare code and unit tests in an isolated environment and save the proposed changes to a working branch.
    A developer designated by the client reviews the pull request and decides whether to approve it.
    Architecture and dependency changes
    The agent can analyze available options, review dependencies, and prepare a proposed change with supporting rationale.
    A tech lead or software architect on the client’s side assesses the impact and makes the decision.
    Merging changes into the main branch
    The agent can prepare the pull request and collect the required test and control results. It does not merge changes independently.
    An authorized client engineer approves the merge after reviewing the changes and quality-control results.
    Production deployment
    The agent can prepare the artifacts, documentation, and information required for the release process. It does not initiate production deployment independently.
    A person authorized by the client approves the deployment in accordance with the organization’s change-management process.

    Security and Traceability Across the Agentic AI Software Development Lifecycle

    Code security does not depend solely on whether the code was created by a developer or an AI agent, but on the controls applied before deployment. Within the client’s environment, we configure testing, security scans, approval rules, and change records so the team can identify risks and trace the history of each modification.

    Security by Design

    We configure the controls required by the organization, such as testing, Static Application Security Testing (SAST), Software Composition Analysis (SCA), and scanning for exposed secrets or sensitive data.

    Audit Trail from Requirement to Release

    The audit trail can link each change to its requirement, commit, test results, approval decision, and the release in which it was included.

    Your Repositories, Policies and Permissions

    We configure the process within the client’s repositories and CI/CD environment. It follows the organization’s existing access, security, code review, and change approval policies.

    Our Certifications

    Our AI and information security management systems are certified to ISO/IEC 42001:2023 and ISO/IEC 27001.

    Audit Trail from Requirement to Release

    01
    Requirement
    02
    AI agent activity
    03
    Repository change
    04
    Testing and security scans
    05
    Review and approval
    06
    Release
    Task source, author, requirement description, and acceptance criteria
    Model and version used, task identifier, and a record of the steps performed
    Commit, modified files, working branch, and identification of changes prepared by AI
    Test results, Static Application Security Testing (SAST), Software Composition Analysis (SCA), and sensitive data scanning results
    Reviewer, date, decision, comments, and any required corrections
    Version number, release date, target environment, approver, and Software Bill of Materials (SBOM)

    Where Agentic AI for Software Development Delivers the Most Value

    Agentic AI for software development delivers the greatest value in projects where tasks can be clearly defined, automated, and verified.

    Legacy Modernization

    We configure AI agents to analyze the existing codebase and prepare changes that support gradual system modernization. The client team reviews the proposals, while regression tests help verify that the application’s existing behavior is preserved.

    Test Automation

    We help configure AI agents to create and update tests and select regression suites within the client’s process. We also develop Qatana, our proprietary Agentic Test Automation Platform, which brings test management, AI-assisted capabilities, and automation results into one place.

    New Products and Large Backlogs

    We configure AI agents to support the client team in preparing code and tests for new features. This can improve backlog delivery without bypassing the organization’s existing quality, security, and change approval rules.

    Engagement Models Tailored
    to Your Engineering Team

    We tailor the scope of our work to the organization’s needs, capabilities, and technology environment.

    MODEL
    BEST SUITED FOR
    SCOPE
    RESULT
    Pilot
    Organizations that want to test the approach with one project or engineering team before considering broader adoption.
    We select a suitable use case, configure the agents and control mechanisms, and define how the results will be measured.
    A validated approach, results from the agreed measurements, and recommendations for the next steps.
    Client-Side Process Setup
    Organizations with in-house development teams that want to introduce AI agents into their existing software delivery process.
    We configure the agents, integrations, and quality controls within the client’s repositories and CI/CD environment. We also prepare the engineering team to work with the new model.
    An operational process tailored to the client’s environment and policies, supported by documentation and team preparation.
    Long-Term Support and Scaling
    Organizations that have validated initial use cases and want to expand the process across additional teams, repositories, or projects.
    We help refine agent configurations, improve control rules, and extend the process to additional areas in line with the organization’s requirements.
    A consistent and continuously improved process for using AI agents across engineering teams while maintaining quality, security, and change approval requirements.

    Let’s Discuss AI Software Factory for Your Engineering Team

    Certified management systems
    ISO/IEC 42001ISO 27001ISO 9001
    Adam Kaczmarczyk

    Technology and Competence Director

      FAQ

      What is an AI software factory, and how is it different from using AI coding assistants?

      An AI Software Factory is a structured operating model that introduces AI agents across the software development process, from a backlog item to release. An AI coding assistant supports an individual developer, while this model also covers testing, quality controls, change approval, and integration with the organization’s repositories and CI/CD environment.

      Is code generated by AI agents secure enough for business-critical systems?

      It can, provided that it passes controls appropriate to the system’s risks and requirements. Before deployment, changes may undergo testing, engineer review, Static Application Security Testing (SAST), Software Composition Analysis (SCA), and scanning for exposed secrets and sensitive data.

      How do you trace which code changes were generated by AI and who approved them?

      An audit trail can link each change to its requirement, the AI agent’s activity, commit, test results, approval decision, and relevant release. This makes it easier to trace a change from a backlog item to production, provide information during an audit, and investigate the source of an issue.

      Who owns the intellectual property of code generated by AI agents?

      Ownership and usage rights are defined in the client agreement, taking into account the project scope, the tools used, and their licensing terms. Provisions concerning the transfer of rights and the use of third-party components should be agreed before delivery begins.

      Can the process run in our environment and follow our policies?

      Yes. The process can be configured within the client’s repositories, CI/CD environment, and infrastructure, following the organization’s access, security, code review, and change approval policies. The exact configuration depends on the architecture, tools, and requirements of the organization.

      How long does it take to introduce the process in a large organization?

      The timeline depends on the pilot scope, CI/CD environment, number of integrations, access policies, and security requirements. We typically start with one team or project, measure the agreed outcomes, and then decide whether to expand the process. We define the schedule after reviewing the environment and selected use case.