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Case study · Research & archive system

An AI archive that answers verifiably

We built an AI archive system that searches fast and answers with evidence: every statement carries a citation, and where proof is missing the system stays silent rather than guess. The archive is sovereign by design and runs on-premise.

The challenge

A wealth of knowledge nobody could find

An archive grown over decades holds an enormous amount of knowledge, but classic full-text search finds only words, not meaning, and common AI searches happily “invent” confident answers. In daily work both are useless: users need the right information fast, and it must be able to rely on every statement.

On top of that, such archives are sensitive and legally exposed. Offloading them to a US cloud was out of the question. What was needed was a system that understands meaning, provides evidence and runs entirely in-house.

  • Decades of articles, barely searchable
  • No tolerance for invented answers
  • Sensitive data, no US cloud egress
RAG · 0-Halluzination

The solution

Answers with evidence, or none at all

Hybrid retrieval Keyword search (BM25) and vector embeddings, fused via Reciprocal Rank Fusion.
Cross-encoder reranking From the top 50 the most relevant passages, only those reach the model.
Citation grounding Every statement NLI-bound to concrete source sentences, with a citation.
Honest abstention Without evidence the answer is “not found”, zero hallucination as a mandate.
Knowledge graph NLI-verified people, organisations and relations, with path-finding.
Fact-check Detects claims and surfaces contradictions in the corpus.
Dossier → manuscript Collect findings, annotate and turn them into a sourced, finished text.
Sovereign & on-premise Data in-house, without US cloud and without US models.
PageIndex Navigable document tree: section → topic → source.

Technology

The stack we used

Retrieval & grounding

  • BM25
  • Vector embeddings
  • Reciprocal Rank Fusion
  • Cross-Encoder-Reranker
  • NLI-Grounding
  • PageIndex

Knowledge & storage

  • Qdrant
  • Neo4j
  • PostgreSQL
  • OCR
  • S3 / MinIO

Quality & ops

  • eval_harness (RAGAS)
  • WORM audit
  • On-premise
  • Keycloak SSO

What matters

0 guessed answers, without evidence “not found”
100 % data in-house, on-premise
0 US models
exact source citations per answer

Trust beats features

The bottleneck in practice is neither search nor text generation, both are commodities today. The bottleneck is trust. A system that confidently cites the wrong source is useless in daily working practice. So this AI archive is not built around the fastest answer but the verifiable one: every statement is bound to concrete source sentences, every answer comes with a citation, and when the archive has nothing, the system honestly answers “not found” instead of guessing.

This stance is built into the architecture. Hybrid search and reranking surface the best passages, and NLI grounding with abstention binds answers to evidence. An NLI-verified knowledge graph maps people, organisations and their relations; a fact-check module exposes contradictions in the corpus instead of smoothing them over. The system is engineered for high citation coverage at zero invalid citations. From the first keyword through a sourced dossier to the finished manuscript, the human stays responsible, the AI supplies the evidence.

And all of it stays sovereign: data remains in-house, the stack runs on-premise, without US cloud and without US models. We build the same stack for archives, law firms, public authorities and research, see research & archive systems.

FAQ

Frequently asked about this case study

How is a wrong answer prevented?

Via hybrid search, reranking and strict grounding (NLI): every answer is bound to evidence; with none it returns “not found”. Faithfulness is measured reproducibly with an eval harness.

Why don't you name the client?

We publish client names only with explicit consent. We're happy to walk you through the architecture and approach in detail in a call.

Can this be transferred to our archive?

Yes. The same stack suits archives, law firms, public authorities and research. We start with a pilot on your real documents.

A verifiable archive for your knowledge?

We set up a pilot in your environment, using your own documents.