ServicesAI integration

Service

AI integration

We connect AI to your data, systems and processes: RAG, agents, automation. Securely, with evaluation and multiple models behind one layer.

RAGAI agentsvector indexesMCPon-prem and cloud

What it involves

Four things that are really part of the work.

  • Mapping the data and processes where AI makes sense at all, and those where it doesn't.
  • A retrieval layer over your data that respects your roles and permissions.
  • Integration with production systems, not an isolated demo nobody will use.
  • Logging of queries and sources and quality evaluation from day one, not once somebody asks.

When it makes sense

You have the data in one place and a process that the person doing it can describe. You know what the process costs today, so it will be possible to calculate whether the change paid off.

When we will talk you out of it

The data is scattered across five systems and nobody vouches for which version is valid. A model will build confident nonsense on that. Start with the data; AI can wait.

How we proceed

After every step you can decide whether to continue.

  1. 01

    Analysis

    We go through the data, processes and constraints. The output is a list of places where it makes sense, ranked by benefit versus effort.

  2. 02

    Pilot

    One process, one team, a measurable result. At the end you can decide whether to continue.

  3. 03

    Integration

    Connection to production systems, permissions, logging, documentation of data flows.

  4. 04

    Operation

    Monitoring answer quality, fine-tuning and development. Or handover to your team.

In practice

On real data, not in a demo.

  • Smart Library: our own AI product for insolvency law. Links to primary sources, semantic search, 100,000+ documents, MCP connector for Claude and ChatGPT.
  • Gepard Finance: RAG over bank methodologies for a network of mortgage advisors. Answers with sources, spend limits, access for external advisors too.
  • KAPITOL: structured data extraction from documents; OpenAI, Gemini and local Ollama behind one layer.

Next step

Does this fit what you are dealing with?

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