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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
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
Who it is for
The service is designed for organizations with in-house development teams facing:
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.
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.
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.
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.
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.
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?
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
Requirement
AI agent activity
Repository change
Testing and security scans
Review and approval
Release
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.
Let’s Discuss AI Software Factory for Your Engineering Team
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.