Retrieval & document intelligence
Search and question-answering across your contracts, policies, claims files or case law, with citations a professional can rely on.
from $18,000
AI for regulated industries
AI that cites its sources, or says nothing at all.
We build production retrieval and agent systems for legal, insurance, healthcare and financial services. Grounded in your documents. Hallucination measured on every build, not estimated after launch. Audited by the engineers who wrote the code.
No obligation, no sales deck. You talk to the engineer who would build it.
Why this is different
In your industry, a confident wrong answer is a liability event.
Most AI vendors optimize for a demo that impresses. That is a different engineering problem from a system a compliance officer will sign off on, and it fails in ways you only discover in front of a client.
Every claim traces to a source
Answers carry citations back to the exact passage. If the corpus cannot support a claim, the system declines rather than improvising.
Accuracy is a build gate, not a dashboard
An automated evaluation runs on every release and fails the build if grounding degrades. Regressions are caught before your users meet them.
Your documents stay yours
Self-hosted retrieval where the mandate requires it. No training on your data, a named subprocessor list, and a DPA we will actually sign.
The people who build it also break it
Penetration testing and secure engineering are in-house. The security review that stalls most AI projects is where we start.
How a question becomes a cited answer
The retrieval path in CourtNetra, drawn as it runs. Scroll to follow it.
- 01
The question arrives
Counsel asks something in ordinary language, usually carrying a filter: a court, a bench, a date range, a statute.
- 02
It splits two ways at once
Dense vector search over 687,289 chunks under HNSW, and lexical BM25 over the same corpus. Legal language is full of exact terms of art that embeddings blur, and citations that lexical search finds instantly.
- 03
The filter pushes down, not after
Partial indexes on the high-traffic filter dimensions, so a narrowed query has a real index to traverse. Applying the predicate after an approximate index returns its candidates is how filtered search silently gets slower than unfiltered.
- 04
Reciprocal rank fusion reconciles them
Two ranked lists, one order. Neither leg has to be right on its own, which is the point of running both.
- 05
Six layers of correction
Adaptive reranking, dynamic context assembly and verification, under a hard thirty second budget. The pipeline can conclude that nothing it retrieved supports an answer.
- 06
A cited answer, or none
Every claim carries a citation to the passage relied on. Where the corpus cannot support a claim, the system declines rather than improvising.
What we do
Three engagements. Scoped, fixed and delivered.
We deliberately do a narrow set of things well. If your problem is not one of these, we will tell you on the first call rather than three invoices in.
AI agents & automation
Agents that triage, extract, route and draft inside your existing systems, with a human decision point wherever the stakes require one.
from $22,000
AI security & assurance
An independent review of an AI system you already run, or one a vendor built for you. Findings written for both your engineers and your board.
from $6,500
Start here
Tell us what breaks if the AI gets it wrong.
That single answer tells us more than a requirements document. Thirty minutes, straight to the engineer who would build it, no sales deck in between.
Typical reply within one business day · Engagements start at $2,500