Team and process analysis
We review your stack, repositories, code review process, tests and security requirements.
AI services
Claude Code and AI rollout for development teams
We help you roll out Claude Code, GitHub Copilot and OpenAI Codex across your development teams. We set working practices, security and code quality rules, train developers and measure results – so that AI in software development speeds up work without lowering quality.
Challenges of AI in development teams
AI tools for developers are evolving very fast. Editor assistants suggest code, while agentic tools such as Claude Code and OpenAI Codex can make changes across many files, run tests and prepare changes for review on their own.
Without rules, problems appear: inconsistent code, security gaps, code snippets leaking into unapproved tools, growing licence costs and no evidence of whether the tools actually speed up work. Some of the team uses AI heavily, others not at all.
This service is for CTOs, team leads and companies developing their own software that want to introduce AI into the software development lifecycle deliberately.
What we put in order
- Choosing tools and models for the team and project.
- Code and data security rules.
- Quality and review standards for AI-assisted code.
- Integration with CI/CD, testing and static analysis.
- Measuring the results and costs of the tools.
GitHub Copilot for teams, Claude Code, OpenAI Codex – scope
We combine experience in building software with hands-on knowledge of AI tools for developers.
Tool selection
We recommend tools (Claude Code, OpenAI Codex, GitHub Copilot) and models, considering licences, costs and data policy.
Configuration and rules
We configure the tools, project instructions, permissions and integrations via the Model Context Protocol.
Quality and security
We combine AI-assisted work with tests (e.g. Playwright), static analysis (SonarQube) and code review in CI/CD.
Developer training
Hands-on workshops on the team’s own repositories: planning changes, working with an agent, review and testing.
Measuring results
We define metrics and check the tools’ impact on delivery time, quality and costs.
How we introduce AI to an IT team
We start with a pilot in one team and then extend proven rules to other teams.
Consultation
We agree goals, the technology stack, security requirements and the team’s experience with AI so far.
Pilot
We choose a team and project, configure the tools and agree metrics for comparison.
Rules and integrations
We prepare instructions, code review rules and integration with CI/CD, tests and static analysis.
Training
We train developers on their own repositories, focusing on good practices and verifying changes.
Evaluation and rollout
We evaluate the pilot results and plan the rollout to further teams.
Technologies
AI and quality tools across the SDLC
We work with AI tools for developers and with the tools that safeguard quality: automated tests, static analysis and CI/CD pipelines in GitHub Actions, GitLab CI/CD or Azure DevOps.
- Claude Code
- OpenAI Codex
- GitHub Copilot
- Model Context Protocol
- SonarQube
- Playwright
- GitHub Actions
- GitLab CI/CD
- Azure DevOps
- Docker
Example use case
A Claude Code pilot in a web application team
A team developing a web application wants to shorten delivery times but is worried about code quality and security.
In the pilot we configure Claude Code with project instructions, style rules and access to tests. AI-assisted changes go through the same Playwright tests, SonarQube analysis and code review as everything else. After a few sprints the team compares delivery times and the number of fixes, and the proven rules are extended to other teams.
An illustrative scenario showing the typical course and scope of such a project.
Frequently asked questions
Is Claude Code safe for company code?
Security depends on configuration and rules. We define which repositories and data the tool can access, which operations it can perform and how its changes are reviewed.
Which should we choose: Claude Code, OpenAI Codex or GitHub Copilot?
It depends on your stack, the way the team works, data policy and budget. The tools often complement each other. We make a recommendation after analysing the team.
Will AI lower code quality?
Not if changes go through tests, static analysis and code review. We design the process so that AI speeds up work without lowering standards.
Do you train developers?
Yes. We run hands-on workshops on the team’s own repositories, covering planning changes, working with an agent, testing and code review.
How do we measure the impact of AI in an IT team?
We agree metrics before the pilot – such as delivery time, the number of fixes after review and tool costs – and compare them after the rollout.
Experience since 2009, now powered by AI
Since 2009
17 years of building enterprise-class web applications, custom business systems and mobile apps.
Industry know-how
Many projects for automotive, logistics, healthcare, food service and catering, construction and finance.
From idea to maintenance
Consulting, design, development, integrations, rollout, training and ongoing growth in one place.
Data security
Solutions designed in line with GDPR and the EU AI Act, with access control and transparent costs.