Service
RAG & Knowledge Engines
A private question-answering layer over your own matters, contracts, filings and research — answers grounded in your documents, with citations, permission-aware, inside your security boundary.
A knowledge engine is a private RAG (retrieval-augmented generation) system over your firm’s own documents: it retrieves the relevant passages at query time and answers from that evidence, with citations a human can verify — permission-aware, and deployed inside your environment so nothing leaves your boundary.
The problem
A knowledge engine, not a chatbot
A raw LLM answers from frozen public training data — it has never seen your documents, can’t cite them, and confidently fabricates (general models were measured hallucinating on 58–88% of legal queries). Naive "upload a PDF and ask" tools break at scale. A real knowledge engine retrieves the right passages from your corpus and forces the model to answer from that evidence, with citations — because for a lawyer, analyst or compliance officer an answer without a verifiable source is unusable.
The solution
Where automation removes the friction
The pipeline (where the accuracy actually comes from)
Retrieval quality — not the model — is the main lever. We build layout-aware ingestion that survives real documents (scanned PDFs, tables, filings, where naive OCR silently corrupts the data), tuned chunking, and embeddings benchmarked on your corpus. Retrieval is hybrid (dense vectors + BM25 keyword, so exact terms like case numbers and tickers aren’t missed) with cross-encoder reranking — which typically improves accuracy 15–30% and often lowers total latency by feeding the model fewer, better chunks.
On top: contextual retrieval (a recent technique that cut retrieval failures by up to 67% with reranking), citations on every answer, and evaluation that separates retrieval quality from answer quality (RAGAS-style context precision/recall + faithfulness) so we know whether a bad answer is a retrieval or a generation problem. Vector store is chosen to fit — pgvector with row-level security for most regulated mid-size corpora, Qdrant/Weaviate/Milvus when scale demands. GraphRAG only where multi-hop relationship questions justify its much higher indexing cost.
Permission-aware by design
This is where 40–60% of enterprise RAG dies before production — not the algorithm, the access control. We capture permissions at ingest and enforce them at query time, built server-side from the user’s identity (never client-supplied), so the model never sees content the user couldn’t. For law firms this maps directly to ethical walls and matter-level confidentiality — no "one big bucket" vector store.
Inside your environment
Self-hostable open-weight models and an in-VPC vector database mean your documents are used for retrieval only — never to train a shared model — and never leave your boundary, with audit trails throughout. A well-fed 7B model with good retrieval routinely beats a 70B model without context, so in-environment doesn’t mean sacrificing quality.
Example workflows we build
- Layout-aware ingestion (PDFs, tables, scans, filings)
- Hybrid retrieval (vector + BM25) with cross-encoder reranking
- Contextual retrieval & citation-grounded answers
- Permission-aware retrieval (query-time, identity-based)
- Retrieval + answer evaluation (RAGAS-style) on your gold set
The results
The commercial impact
Our approach
From manual to automated
- 01Map corpus & access rules
We profile your documents and how permissions/ethical walls must be enforced before any build.
- 02Build the retrieval pipeline
Layout-aware ingestion, tuned chunking, hybrid retrieval + reranking, citations — benchmarked on your data.
- 03Enforce permissions & evaluate
Query-time access control from identity; RAGAS-style retrieval + answer evaluation on your gold questions.
- 04Deploy in your environment
Self-hostable models + in-VPC vector store; retrieval-only on your data, with audit trails.
Why a custom build beats off-the-shelf
- Retrieval tuned and benchmarked on your corpus — not a generic wrapper.
- Permission-aware: respects ethical walls and matter-level access at query time.
- Self-hostable; your documents are retrieval-only and never train a public model.
- Citations + grounding + evaluation, because RAG reduces but doesn’t eliminate hallucination.
Frequently asked questions
Does RAG stop the AI from hallucinating?
It reduces it substantially but does not eliminate it — even purpose-built legal RAG tools were measured at 17–33% hallucination. That’s why we ground every answer in citations, run faithfulness checks, and keep a human in the loop on high-stakes use. The citation is what makes the residual error catchable.
How is this different from just using ChatGPT?
A public model has never seen your documents, can’t cite them, and can’t respect your access rules. A knowledge engine retrieves from your own corpus, answers with verifiable citations, enforces permissions, and runs inside your environment.
Can it respect who’s allowed to see which documents?
Yes — permission-aware retrieval is core. We capture permissions at ingest and enforce them at query time from the user’s identity, so the model never surfaces content a user couldn’t see. This maps directly to ethical walls and matter-level confidentiality.
Can it handle our scanned PDFs, tables and filings?
Yes — layout-aware ingestion with table-structure recognition, because naive OCR silently corrupts data (associating figures with the wrong entity). Ingestion quality is where most enterprise RAG quietly fails.
How do you measure accuracy?
We evaluate retrieval and answer quality separately (RAGAS-style context precision/recall + faithfulness) against your own gold questions, so we can tell whether a bad answer is a retrieval or a generation problem and fix the right thing.
What does it cost?
Engagements are fixed-price and scoped to the outcome. Every engagement is fixed-price with ROI targets agreed up front, backed by our 90-day ROI guarantee. Book a free audit for a clear price and ROI estimate.