AI Solutions & RAG Systems

Retrieval, extraction and the pipelines around them — built on your own documents.

We build AI systems that run on a company’s own material: retrieval-augmented generation over internal documents, structured extraction from unstructured input, and the pipelines and evaluation around both. In production, this has included automated processing and evaluation of incoming invoice data with LangChain.

We start on your real documents and on a written definition of what correct means for the task — fields, target accuracy, and behaviour in the uncertain case — because that is what the system is later measured against. Deployment runs in your own cloud account. Where a query, a rule or an interface solves the problem more reliably than a model, we say so.

Engagement model. Longer engagements, typically 3–12 months, part- or full-time. Remote or on site in the Munich area. German and English. We work inside your team and your repository, with responsibility for an area end to end.

Our Focus Areas in AI & RAG Systems

  • RAG on your own documents
    Retrieval over contracts, invoices, tickets, manuals or an internal knowledge base, with sources attached to every answer. Chunking, embedding and retrieval strategy chosen for your document structure rather than a default.
  • Structured extraction
    Unstructured input into structured records — invoices, delivery notes, forms, reports — including parsing, cleaning, deduplication and handling of documents that deviate from the expected format.
  • The pipeline around the model
    Ingestion, preprocessing, embedding, indexing and re-indexing on document change, with the index kept consistent against the source of truth.
  • Integration
    The AI function delivered into the systems where the work already happens — internal tools, portals, ticket systems — through existing APIs, or new ones where they are missing.
  • Evaluation
    A test set built from your own material so accuracy is measurable before rollout, and logging that keeps answers traceable afterwards.
  • Guardrails
    Fallback behaviour when retrieval returns nothing, and defined limits on what the system decides without review.

In production

  • RAG-based invoice processing
    Automated processing and evaluation of incoming invoice data, built with LangChain: intake, extraction, structured output into the company’s own workflow system, with the surrounding pipeline for preparation and indexing. Built inside the workflow management system it feeds, rather than as a separate tool.

Technologies: LangChain · RAG pipelines · vector search & embeddings · Python/FastAPI · Node.js/NestJS · Kafka · Apache Beam · PostgreSQL · Redis · Docker · Kubernetes · AWS · GCP

Describe the process and the documents involved.

You get an assessment of feasibility and scope — including where the approach does not fit.