[ PRIVILEGE-SAFE AI FOR LAW FIRMS ]
Search and question your matter library with AI, without uploading client files to a public tool. The retrieval layer is self-hosted on your infrastructure; the AI runs under your own BAA with model training disabled and Zero Data Retention. The same frontier models, owned instead of rented.
30-minute call. We'll review your matter data and privilege requirements.
100ms
retrieval across 1M+ documents
ZDR
training disabled, nothing retained
6–10 wk
to production
[ THE PROBLEM ]
The question is whether they do it in a way that survives a privilege challenge.
Early court decisions have found consumer AI use not privileged. Pasting client material into a public tool exposes it to a provider that may access or train on it. The exposure is access, not only retention.
Harvey, CoCounsel, and similar tools charge per seat, every year, for a layer on top of a model you could contract with directly. You rent the wrapper and never own it.
When there's no sanctioned tool, attorneys use their own. The fix is a firm-owned system that is faster and safer than the consumer tool they'd reach for anyway.
[ WHAT WE BUILD ]
The same workflows as the per-seat tools, on the same frontier models, under your own privilege-safe architecture.
[ FAQ ]
Self-hosted RAG (Retrieval-Augmented Generation) is AI-powered document search where the retrieval and index layer runs on the firm's own infrastructure, so client files are not uploaded to a public AI tool. When the firm asks a question, the system finds the relevant passages in its own documents and passes only those to the model.
Any AI generation runs under the firm's own Business Associate Agreement with model training disabled and Zero Data Retention.
It can. Early court decisions, including matters where consumer AI use was found not privileged, show that pasting client material into a public AI tool can expose it to a provider that may access or train on it. The exposure is access, not only retention.
The defensible posture is to keep retrieval inside firm infrastructure and run generation under a contract with model training disabled and Zero Data Retention, so client content is never used to train a model and is not retained after the request.
It targets the same workflows, document Q&A, drafting from precedent, matter research, but the firm owns the system instead of renting a per-seat wrapper. It runs on the same frontier models, under the firm's own BAA and Zero Data Retention, for a fraction of the lifetime cost of a per-seat subscription.
We compete with the markup layer, not the model.
The search and index layer is self-hosted, so your documents never leave your servers for retrieval. For the AI generation step, the relevant passages are sent to the model provider for inference; we run that under your own BAA with training disabled and Zero Data Retention.
Full data locality, where nothing leaves at all, is only true with an on-premises or local model, which we can also build where the use case justifies it.
Builds ship in 6 to 10 weeks and start at $17,000, with most firm projects landing between $17,000 and $53,000 depending on document volume, integrations, and how much workflow automation you want. We scope it for free and quote a fixed price before you commit.
[ GET STARTED ]
Book a 30-minute review. We'll look at your matter library, your privilege requirements, and what it takes to build a retrieval system the firm owns, whether you hire us or not.
Free architecture review included
