AI integration
AI that answers from your data, with measured quality and predictable cost — instead of a flashy demo that never reaches production.
Sound familiar?
Company knowledge is scattered across PDFs, Confluence and email — nobody finds it.
The chatbot hallucinates and there’s no way to evaluate answer quality.
Model API costs grow without control.
Repetitive document work (classification, extraction) ties up people.
What it usually looks like inside
- Userchat · API · agent
- APIauth · rate limit
- LLM routercost ↔ qualityRetrieverhybrid + rerank
- ModelsClaude · OllamaSemantic cacheRedispgvectorembeddingsKnowledgePDF · Confluence · DB
The scope you get
RAG on your data
Hybrid search + re-ranking, source citations, access control.
Tool-using agents
Agents calling your APIs and systems (MCP), with human-in-the-loop.
Model routing
A cheaper model where it’s enough, a stronger one where it matters.
Evaluation
Test sets and metrics (e.g. RAGAS) — quality in numbers.
Privacy
Local models (Ollama) or EU hosting when data can’t leave the company.
FinOps
Semantic cache, limits and a cost dashboard per feature.
Technologies in this area
- Python
- FastAPI
- PostgreSQL
- Redis
- Claude
- Ollama
- LangChain
AI integration — FAQ
01Where do we start?
With one process that has a measurable outcome. We build a proof of concept on your data and evaluate quality and cost.
02Will my data go to OpenAI/Anthropic?
Only if you agree. Alternatively I use local or EU-hosted models.
03How do you measure quality?
We build a set of questions and expected answers, and every change is tested automatically (faithfulness, relevance, cost, latency).
Got a project that has to be fast and work at scale?
Describe it in 2 minutes. I'll reply within 24 hours with first insights and a proposed next step.