Private AI
Running retrieval and inference entirely inside your infrastructure.
Private AIEnterprise RAG
Retrieval built for enterprise conditions — heterogeneous documents, per-user permissions, content that changes daily, and an accuracy bar a demo pipeline will not meet.
The problem
Why naive RAG fails in the enterprise
Retrieval-augmented generation is easy to demonstrate and hard to productionize. The tutorial version — chunk documents, embed, retrieve top-k, generate — collapses on contact with a real corpus.
Permissions are the first wall. In a demo, everyone sees everything. In an enterprise, access varies by user, by document, and by matter or case. A system that indexes everything and filters afterwards has already aggregated material across boundaries it should never have crossed.
Retrieval quality is a data engineering outcome, not a model one. Inconsistent formats, tables, scanned documents, near-duplicates, and superseded versions all cap accuracy regardless of which model generates the answer.
And without evaluation, "good enough" has no meaning — which is why so many RAG pilots demo well and never ship.
Scope
What we deliver
- Ingestion and chunking pipelines — Format-aware processing for the documents you actually have — PDFs, scans, tables, email, and structured records — not just clean markdown.
- Embedding and retrieval strategy — Tuned on your corpus, with hybrid lexical and semantic search where pure vector similarity underperforms. Measured, not assumed.
- Identity-aware access control — Entitlements enforced at query time against your existing directory. A user cannot retrieve through AI what they could not open directly.
- Citation grounding — Every assertion traceable to a source passage, so users verify rather than trust — the single biggest driver of adoption in regulated settings.
- Incremental re-indexing — Content changes continuously. Full rebuilds do not scale, so the pipeline handles updates, deletions, and supersession.
- Retrieval evaluation harness — A maintained test set with agreed thresholds, plus regression testing in CI so quality does not quietly decay.
Detail
Where accuracy actually comes from
| Lever | Typical impact |
|---|---|
| Document processing quality | Largest single factor. Badly extracted tables and scans cap the ceiling before retrieval begins. |
| Chunking strategy | High. Semantic boundaries beat fixed windows; the right size is corpus-specific and must be measured. |
| Hybrid search | High for corpora with jargon, part numbers, or citations, where lexical matching outperforms embeddings. |
| Reranking | Moderate to high. Frequently the cheapest meaningful gain once retrieval is working. |
| Model choice | Lower than expected. Usually the last lever worth pulling, not the first. |
Questions
Frequently asked
How do you stop it returning things people should not see?
Enforce entitlements at query time, not at index time, and never by filtering after retrieval. The check sits in front of the retriever, against the same source of truth your document system uses.
How accurate is it?
That is a question about your corpus, not about the technique. We build an evaluation set from your documents and real queries, measure against it, and agree acceptance criteria before development. Any vendor quoting an accuracy figure without seeing your data is guessing.
Do we need a vector database?
Often, but not always. For modest corpora, pgvector alongside your existing Postgres avoids a new system to operate. We recommend against adding infrastructure you do not need.
What about documents that change constantly?
Incremental indexing with supersession handling. The failure mode to avoid is confidently answering from a document that was replaced last month.
Related
AI Security
Entitlement bypass, injection through retrieved content, and adversarial testing.
AI SecurityStart with an assessment, not a proposal.
A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap you own.
Direct response from an engineer. Typically within one business day.