Generic AI answers fluently and sometimes wrongly, and in a company the difference is expensive: a wrong clause, an outdated procedure, a price from last year. Trust dies the first time someone catches an invented answer.
We build retrieval pipelines with multiple search branches, reranking, temporal validity, and citation checks after generation. The system says where every answer comes from, or says it does not know.
Document Q&A over contracts, manuals, procedures, and archives, with permissions inherited from your systems.
Post-generation verification of citations, existence, and validity. What cannot be verified is flagged, never presented as certain.
On-premise, private cloud, or air-gapped. Open-weight or commercial models, chosen on data sensitivity and cost.
PDFs, scans, tables, and legacy archives parsed with structure preserved.
Lexical, vector, and metadata search, fused and reranked.
Post-generation checks on existence, source, and validity.
Every answer respects the permissions of who is asking, inherited from your systems.
Real question sets measured on every pipeline change.
Open models served on your infrastructure when data cannot leave.
Fixed scope approved before we start, milestone payments, code that stays yours, and an engagement that ends only when the system runs in production.
The Fabrica guarantee
PostgreSQL
Qdrant
Pinecone
Elasticsearch
Redis
Anthropic
OpenAI
Mistral
Hugging Face
FastAPI
Python
DockerA production RAG system controls how documents are parsed, chunked, indexed, and retrieved, enforces permissions, and verifies citations. A chatbot with attachments does none of that.
Only if you decide so. The whole pipeline can run inside your infrastructure, including the models.
With an evaluation set built from your real questions before going live: retrieval recall, citation accuracy, and refusal correctness, measured on every change.
Bring a folder of real documents and ten real questions. That is how every knowledge project starts.
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