A general chatbot guesses, and on a contract or a filing a confident guess is the expensive failure. We build retrieval and extraction where every field traces to a span a reviewer can open, anything outside the approved index is refused rather than improvised, and a person stays in the loop on the call. The output is something a compliance team can put its name to.
01 / DOC INTELLIGENCE
For teams whose answers get checked. Extraction and search where every field traces to a span in the source, and anything outside the approved index is refused.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
On Brandiligence the model physically cannot cite an authority outside the firm's approved index, and a validator rejects every draft that tries. Fabricated citations fell roughly 90%.
Brandiligence was our first GenAI build and is still live, shaped daily by a practicing trademark attorney who signs off on its drafts. We have grounded models on regulated documents longer than most teams have used them.
DocVerse runs agentic search over dense ESG and IMF filings, grounds every answer in a citation, and fact-checks new documents against the knowledge base, on filings general chatbots get wrong.
Week 1
Regulated documents are messy: scanned PDFs, inconsistent structure, domain language. We start by understanding the corpus and the questions that have to be answered correctly, every time.
Week 1–3
Hybrid search, reranking, and chunking tuned for your documents, with a citation tied to every extracted field so there is a real source behind each claim.
Week 3–4
Validators reject anything outside the approved knowledge base, and new documents are fact-checked against it. The model is never trusted to be correct on its own.
Week 4–6
A review surface keeps your team in control of the final call, with quality measured by real evals rather than a sense that it seems better.
Legal & IPIn production
A drafting engine for trademark clearance opinions. It holds the firm's five-part structure on every draft, can only cite authorities from a curated index, and rejects its own output when a citation does not resolve, so the attorney reviews and signs rather than writing from scratch.
ESG & sustainabilityIn production
An ESG-intelligence pipeline that ingests dense, inconsistent filings at scale, extracts framework-aligned metrics with per-field confidence and source lineage, connects them across reports and years, and serves them through a governed analytics layer where a low-confidence number goes to review rather than into a dashboard.
Market intelligenceIn production
A reasoning-first prospect intelligence engine that turns a company or LinkedIn URL into a structured, intent-aware profile. Multi-source ingestion and hybrid retrieval surface business model, ICP fit, strategy and risk, with generation constrained to evidence the system actually retrieved.
A page has to be read as a layout before it can be split, because a table that loses its column headers is worse than no table. Chunks are vectorised against an index, a schema-constrained pass pulls fields, tables and entities with a confidence score, and nothing leaves without a citation pointing back at the span it came from. Low confidence routes to a human instead of guessing.
Production-proven
Built by engineers who've already shipped this in production.
That is the whole problem we engineer against. Answers are grounded in citations, validators reject anything outside your approved sources, and new documents are fact-checked against the knowledge base. On a regulated legal build this cut fabricated citations roughly 90%, and a validator catches the rest.
Yes. We tune OCR, chunking, and ingestion for messy, real-world corpora, the scanned filings and inconsistent forms that break naive RAG, instead of assuming clean text.
Your team. The system surfaces citations and a review step so a human signs off on the final call. It is built to make an expert faster, not to replace their judgment.
Less than teams expect — a few hundred reviewed examples from your own corpus is usually enough to establish a baseline and catch the failure modes that matter. We build that set with you in the first fortnight.
New documents are fact-checked against the existing knowledge base rather than trusted on arrival, which is how DocVerse catches drift. Format changes surface as validation failures instead of silently wrong fields.
You do. The index sits in your infrastructure and the ingestion pipeline is yours, so you are never in a position where switching providers means re-processing the corpus from scratch.
Evals, routing, and audit trails for teams whose demo cannot survive real traffic.
View serviceMulti-step agents for workflows where a wrong action is expensive to undo.
View serviceThe product surface around your model, for teams who have the AI but not the app.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.