AI Implementation for Companies
We help companies effectively implement AI tools into the daily work of technical teams. Not theory — a concrete plan, tool selection, process integration, and results measurement.
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You have AI tools
but don't see results
Just buying a Copilot license isn't an AI implementation. Without proper setup, training, and processes — tools go unused.
Purchased licenses
go unused
6 months of Copilot in the company, and developers revert to their old workflow after a week. Without practical workshops and ready-made patterns — adoption doesn't happen.
No policies
= chaos and risk
What goes to an external AI model? What data is sensitive? Without AI security policies, tools create a risk of code or customer data leaks.
You don't know if AI
actually speeds up work
Management asks about ROI from the AI investment. Without measured KPIs (velocity, PR review time, test coverage) the answer is always "we think it helps."
* McKinsey State of AI, GitHub Copilot Impact Study, Gartner AI Adoption Report
AI implementation
tailored to your team
From small teams starting with AI to large organizations needing full CI/CD integration, security policies, and results measurement.
Starter Package
(3–10 devs)
Quick AI start for a small team — needs audit, selection and configuration of one tool, practical workshop, and usage playbook.
- Audit of current tools and processes
- Selection and configuration of one AI tool
- Practical workshop (4h) for the team
- Playbook: when and how to use AI
Professional Package
(10–50 devs)
Full AI implementation for a mid-sized team — tool set selection, CI/CD integration, per-team workshops, and results measurement after 4 weeks.
- Full development process audit
- Selection and integration of AI tool set
- Workshops for individual teams
- Integration with GitHub/GitLab and CI/CD
- KPI report after 4 weeks of usage
Enterprise
(50+ devs)
Enterprise-scale AI implementation — security policies, data isolation, on-premise or private cloud, dedicated adoption program, and quarterly reviews.
- Everything from Professional Package
- AI security policies for the organization
- On-premise / private cloud configuration
- Adoption program and change management
- Quarterly reviews and optimizations
How do we implement AI in a team?
From audit to measurable results —
concrete KPIs visible after 4 weeks of use.
AI readiness audit
We analyze current processes, tech stack, tools in use, and team AI knowledge level. We identify where AI will have the greatest impact.
Selection and configuration
We select tools matched to your stack (Copilot, Cursor, Claude Code, JetBrains AI). We configure integration with IDEs, GitHub, and CI/CD pipelines.
Practical workshops
We run workshops for developers: prompting for programmers, AI-assisted code review, refactoring with AI. Playbook for when to use AI and when not to.
Measurement and optimization
We track KPIs: velocity, PR review time, deploy time, test coverage. Report after 4 weeks with findings and optimizations for further adoption.
What will your team gain
after AI implementation?
Real productivity, not licenses
Developers who know how to use AI effectively are 20–55% faster on repetitive tasks. Implementation delivers this change — just buying tools doesn't.
Safe AI usage
AI security policies, configuration of what goes to external models, sensitive data isolation. Use AI without the risk of code or customer data leaks.
Measurable KPIs, not feelings
We track sprint velocity, PR review time, commit-to-deploy time. Report after 4 weeks shows exactly what changed — numbers for the management conversation.
Lasting adoption, not one-time hype
Playbooks, policies, and habits built during implementation stay in the team for years. 4–8 weeks of post-implementation support — so you don't revert to the old workflow.
Your team has Copilot but doesn't know how to use it?
Just buying licenses isn't implementation. We help build the workflows, prompts, and processes that actually speed up work — and we measure the results.
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