AI in operations
We bring AI where it delivers measurable impact: start with a pilot, assess economics and risks, and only then scale.
Challenges we solve
- You want to use LLMs but don't know where to start or where the payoff is
- The knowledge base is huge and employees struggle to find answers
- Developers spend time on routine: documentation, reviews, untangling legacy code
- The security team is worried about data leaking into models
What's included
01Use-case discovery
Where AI will save time or money — with an impact estimate before you start.
02Knowledge-base assistant (RAG)
Search across documentation that respects access rights: vector and full-text search, answers with source links.
03AI in development
Documentation generation, code analysis and refactoring, code-review assistance.
04Data security
Access-rights filtering before data reaches the model, a human in the loop for critical decisions.
05Quality assessment
Test question sets, escalation rate, user feedback.
06Model choice
GigaChat, YandexGPT, Claude and open models — to fit data requirements and budget.
Business outcomes
Experience
The founder has launched a range of AI scenarios: a RAG assistant over a Confluence knowledge base (pgvector, hybrid BM25 + vector search, access-rights filtering before the LLM, human in the loop), plus documentation generation and code analysis and refactoring with Claude and GigaChat.
We're upfront that these are pilots: we start small, measure impact and scale only what proves itself.
Engagement formats
- IT assessment — use cases and impact estimate
- Pilot — per scope
- Fractional CTO — rollout support
Technologies & methods
Tell us about your challenge
We'll reply within one business day and suggest a format: assessment, strategy or ongoing support.