Build model
Fixed scope
know everything
RAG: ask 10,000 documents, get cited answers
We build retrieval pipelines over your contracts, policies, and project history: ask in plain English, get the answer plus the paragraph it came from. Enterprise search gets it right on the first try about 10% of the time — retrieval with citations closes that gap on your own data.
Timeline
2–8 weeks
Runtime
Monitored
Handover
Owned by your team
Delivery blueprint
Visual summary of what this build includes from quote to handover.
Scope
Written
Deliverables and acceptance checks are fixed before build.
Execution
Instrumented
Retries, logs, and alerts are configured for production.
Controls
Approved
High-risk actions are gated by named human approvers.
Transfer
Owned
Runbook and account ownership move to your team at launch.
Search finds documents. You need answers.
Enterprise search succeeds on the first try about 10% of the time — against Google’s 95% on the open web. So your team opens 6 near-identical PDFs, skims all of them, and still isn’t sure which version of the contract is current.
The obvious fix — a chatbot pointed at your files — creates a worse problem: confident wrong answers. An LLM without retrieval guardrails will invent a clause that isn’t in the contract, cite a policy that was never written, and quote a price from 2 revisions ago. For legal, finance, and compliance questions, an uncited answer is a liability, not a feature.
What we build
Ingestion + chunking pipeline
Parsers for PDF, DOCX, spreadsheets, and email; document-aware chunking; embeddings written to Postgres + pgvector. n8n keeps the index in sync on a schedule, so new documents are answerable within the hour.
n8n · Postgres · pgvector
Citation-first retrieval API
Hybrid keyword + vector search with reranking, then Claude writes the answer constrained to the retrieved passages — every claim links back to its source paragraph. Low retrieval confidence returns “not found in your documents”, never a guess.
Claude · Postgres · pgvector
Interfaces where questions happen
A Slack bot, a web chat, or an endpoint inside your existing internal tool — your team asks where they already work, and every answer arrives with clickable sources.
Slack · Next.js
ROI math
10 document hunts/day × 12 min saved × $40/hr × 250 days = $20,000/yr recovered — typical build $6–20k.
Search-time savings use published knowledge-work medians, typical for this type of automation — the audit replaces them with your own numbers.
How this ships
Week 1
Audit
Question set — the 25 questions your team actually asks — plus a corpus inventory with formats, owners, and access rules
Week 2
Blueprint
Retrieval architecture: chunking strategy, index design, permission model, and the eval criteria the build must pass
Weeks 3-4
Build
Working pipeline answering the eval set with citations, live in Slack or the browser
FAQ
What does a RAG build cost?
A single-corpus pilot — one document set, one interface — runs $2–6k. Multi-source builds with permissions and evals land in the $6–20k band. Fixed written quote after a free audit; ongoing cost is hosting plus model usage, typically $40–180/mo.
How fast can it be live?
A pilot on one document set is typically live in 2–3 weeks; a multi-source build in 4–6. Either way, the first working version answers real questions inside 30 days.
What document formats and sources can it read?
PDF, DOCX, spreadsheets, HTML, and email out of the box; Google Drive, SharePoint, Notion, Confluence, and Zendesk as sources. Scanned PDFs need an OCR pass — we flag those in the audit so the quote already covers them.
Is our data safe? Does it train models?
The index lives in Postgres on your own cloud account, encrypted at rest. Answers run through the Claude API under commercial terms — your documents aren’t used for model training. Retrieval enforces per-user permissions, so people only get answers from documents they can already open.
Get a fixed quote for a RAG build
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