Prepare the knowledge
Parse representative documents, preserve structure and choose chunking and metadata based on the questions the system must answer.
page · section · table kept with every passage02 / INTELLIGENCE / PRODUCTION RAG
Answers that lead
back to evidence.
Retrieval-augmented generation systems built around your documents, access rules and real evaluation questions.
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THE STARTING POINT
The demo answers. Can it be trusted?
A fluent answer is not enough. Documents change, permissions differ and some questions have no supported answer. Retrieval needs to be evaluated as carefully as generation.
WHAT WE WORK TOWARDA retrieval system with inspectable sources, defined access boundaries and a repeatable evaluation process.
Can contractors expense travel to client sites?
Yes. All travel booked for client work is reimbursable.
- sources cited0
- policy_handbook_v2.pdfsuperseded
- asked bycontractor
- evaluation setnone
looked right in every demo4 problems nobody can see
WHAT THE WORK INCLUDES
SCOPE IS DEFINED TOGETHERRetrieve the evidence
Build and compare retrieval approaches. Apply source-level access rules and carry provenance through to the answer.
answer → [§4.2 p.7] [§4.3 p.7] · access checkedEvaluate the whole path
Measure retrieval separately from answer quality. Test missing evidence, conflicting sources, stale content and out-of-scope questions.
retrieval scored apart from the answerWHERE IT FITS
Recognize the problem?
Internal knowledge search
Help a team locate and understand approved information spread across documents.
Technical document assistance
Retrieve passages from manuals, specifications and procedures with links to their source.
RAG prototype review
Identify whether a weak result comes from parsing, retrieval, context assembly or generation.
Is this just a chatbot?+
The interface can be conversational, but most of the engineering is beneath it: ingestion, indexing, access, retrieval and evaluation.
Can RAG eliminate hallucinations?+
No. Retrieval can provide evidence, but it does not guarantee a correct answer. We design for citations, uncertainty, abstention and evaluation against representative questions.
How do you choose models and databases?+
We compare candidates against the corpus, questions, latency constraints, operating costs and deployment requirements. We do not begin with a mandatory vendor stack.