Sources + golden set
We map your sources and build a golden dataset — the questions that must be answered right — before writing a line of retrieval.
AI Knowledge Assistant
Your company knowledge, answered with citations — a production RAG pipeline with permissions, evals, and monitoring.
- Price
- From $25K
- Timeline
- 4–8 weeks
- Terms
- Fixed scope
We build retrieval that cites its sources, respects permissions, and improves over time.
Ingestion, embeddings, hybrid retrieval, reranking, a citation format, a permissions model, a golden dataset and eval harness, a feedback loop, and monitoring — across four to eight weeks.
The opposite of chat-with-docs: a knowledge system tuned against a golden set you own, so answers are traceable, scoped, and get better instead of silently rotting.
Ingestion + retrieval
Ingestion, chunking, embeddings, hybrid retrieval, and reranking — tuned against the golden set, not vibes.
Citations + permissions
A citation format, a permissions model, and the guardrails that keep retrieval honest and scoped.
Evals + monitoring
The eval harness, a feedback loop, and monitoring — so retrieval improves over time instead of rotting.
An answer with a source. A handoff with context.
Explore an illustrative workflow. This is a design example, not a live agent, client deployment or measured result.
Human ownershipHuman reviewReview requested Illustrative interface · no real customer data
The decision belongs to a person
“Is the repair covered?”
Coverage needs the team’s judgement.
The caller asked for a callback.
Included in the handoff
Request · Call context · Callback details
A person takes over.
The request travels with its context. Ownership is explicit.
Approved knowledgeTeam knowledgeApproved sources Illustrative answer and source · not live retrieval
What happens if a caller asks about coverage?
Answer
Hand the request to the service manager. Do not make a coverage promise.
An answer with its source.
The reference belongs beside the answer, not behind a claim of confidence.
What the engagement covers
- Source connectors
- Structure-aware chunking
- Incremental updates
Your knowledge stays fresh and chunked so retrieval actually finds the right passage.
- Embedding model choice
- Hybrid vector + keyword
- Metadata filters
Retrieval finds the right passage even when the wording doesn't match.
- Cross-encoder rerank
- Relevance tuning
- Top-k discipline
The best passages rise to the top, so the model answers from signal, not noise.
- Inline source links
- Passage-level provenance
- 'I don't know' honesty
Every answer is traceable to a source — and it says so when it can't find one.
- Row / doc-level ACL
- Per-user retrieval
- No cross-tenant leakage
Users only retrieve what they're allowed to see — no leakage across tenants.
- Golden dataset
- Recall + precision scoring
- Thumbs → training
Retrieval is measured and improves over time, instead of silently degrading.
What you receive
Tangible artifacts you keep, whether or not you continue past this engagement.
Deliverables · 11 included
- 01Source ingestion pipeline
- 02Embedding workflow
- 03Hybrid retrieval
- 04Reranking
- 05Citation format
- 06Permissions model
- 07Golden dataset
- 08Eval harness
- 09Feedback loop
- 10Monitoring
- 11Runbook
A retrieval system, delivered cited and evaluated — not chat-with-docs.
The pipeline that turns your knowledge into cited answers, the permissions that keep it honest, and the evals that keep it improving. A sample is shown; yours runs on your corpus.
A cited answer
The model answers from your sources, with inline provenance — and refuses when it can't.
RequestStep 01
What's our refund window for enterprise?
The run record keeps the request beside the response.Illustrative workflow. Select a stage to inspect it; this is not a client run.
Retrieval evals
Recall and precision against a golden dataset, tracked over time.
Golden setEvidence before a verdict
Not runRecall @5
A representative input, expected outcome and execution trace are required before this dimension receives a result.
No grade or performance claim is shown before the buyer's evaluation.
The pipeline
Ingestion to answer, every stage tuned against the golden set.
structure-aware
- Input
- Representative case
- Evidence
- Trace and expected outcome
The states demonstrate the review UI. They are not evaluation results.
What we measure
Agree the baseline, source and acceptance criteria before release.
- Baseline
- Measured with the buyer
- Source
- Named system of record
- Acceptance
- Agreed before release
Targets and observed results belong to the qualified workflow, not a sample dashboard.
Illustrative product scenes · never client results
Plus: A golden dataset you own · a monitoring dashboard · 30 days of async Q&A.
Built for
Replacing keyword search over docs
Answers with citations your team trusts — not a search box that returns ten links.
Grounding an agent in proprietary knowledge
The retrieval layer a custom agent needs to answer from your data reliably.
Retrieving over sensitive knowledge
Permissioned, cited retrieval that respects who's allowed to see what.
Questions, answered
Common questions
Start with one useful decision.
Bring the workflow and the person responsible. We scope the exact number with you, and sign a mutual NDA before any code or data is shared.
From $25K · 4–8 weeks