Industry

Legal and compliance.
Cited, or nothing.

Review, research and discovery, built so that every finding links to the span it came from and the judgement stays with a lawyer. An uncited proposition is worse than no answer at all.

Last reviewed 4 September 2026 · Engineering guidance, not legal advice.

WHERE IT BREAKS TODAY

Fabricated authority

A general-purpose model will produce a plausible citation from memory. In legal work a fabricated citation or invented clause is not an inconvenience — it is professional-liability exposure for the lawyer who files it.

The data room does not fit

Even the largest context windows do not hold a data room. Tens of thousands of PDFs need retrieval that finds the right passage, not a model asked to read everything at once.

Client material cannot leave

Privileged litigation material and confidential client records make public AI endpoints unusable without a deployment topology that keeps the documents where they are.

OUR APPROACH

Candidate issues, with the evidence attached

Legal work is governed by language, which makes it a natural fit for language models and an unforgiving one. We build retrieval-grounded systems over your own corpus — contracts, precedent, briefs, licensed authority — so that answers are generated only from retrieved passages and every claim links to the span it came from. A lawyer starts from candidate issues with the evidence in front of them rather than from the whole document.

That does not make hallucination impossible; no architecture does. It makes an unsupported claim visible, because it has no citation, and it makes a citation that does not resolve structurally unable to reach a user. A real passage can still be misread, which is why the span is shown and the proposition is read at source. The judgement stays with a lawyer.

HOW IT DEPLOYS

PHASE 01

Index the corpus

Your matter history, contracts and briefs are indexed into a private retrieval layer on infrastructure you control, subject to the licence terms of any published authority.

PHASE 02

Encode the playbook

Your standard positions for NDAs, MSAs and DPAs become explicit, versioned, machine-checkable rules — work with value independent of any AI system.

PHASE 03

Review at scale

Lawyer-supervised review agents flag deviations and link every finding to the source span, so an associate verifies rather than reads from the beginning.

WHAT WE BUILD

Grounded Research Systems

Citation-enforced retrieval over your matter history and licensed authority, where a citation that does not resolve cannot reach the user and every proposition links to the passage it rests on.

SEE RAG SYSTEMS →

Review Agents (Lawyer-Supervised)

Review of NDAs, MSAs and DPAs against your own playbook, reporting each deviation with the position, the quoted language and the exact span, for a lawyer to confirm or dismiss.

SEE CONTRACT REVIEW →

Secure Data Vaults

On-premise or sovereign-cloud deployment, so client litigation data has no route to a public endpoint and cannot reach a third party's training data. That is a property of the network topology rather than a promise.

SEE SECURE PIPELINES →

M&A Due Diligence

Extraction of change-of-control provisions and financial liabilities across data rooms containing tens of thousands of PDFs, each finding anchored to its page, for the deal team to review.

SEE WORKFLOW AUTOMATION →

CONTROLS WE IMPLEMENT

GDPR-ALIGNED CONTROLS CLIENT DATA INSIDE YOUR PERIMETER MATTER-SCOPED ACCESS ON-PREM DEPLOY NO THIRD-PARTY EGRESS NO TRAINING ON YOUR DATA

These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and whether privilege is preserved in a given deployment is a question for your counsel — see our trust centre.

DESIGN TARGETS

CITATION PER EXTRACTED CLAIM

DOCUMENTS SENT TO PUBLIC ENDPOINTS

100%

OF ANSWERS LINK TO A SOURCE PASSAGE

Properties we design and build to, not measured client outcomes.

LEGAL USE CASES

LEG.01 Contract Review Clause extraction and deviation detection against your playbook, with the source span shown for every finding. → LEG.02 eDiscovery Review at document volumes where sampling is the only defensible method, and the defensibility is the deliverable. → LEG.03 Legal Research Retrieval over authority where an uncited proposition is worse than no answer at all. →

QUESTIONS

QUESTIONS — 4

We build retrieval-grounded systems over your own corpus. Instead of generating answers from model memory, the system retrieves passages from your indexed contracts, briefs and licensed authority, and every claim links to the passage it came from. That does not make hallucination impossible — no architecture does — but it makes it visible: an unsupported claim has no citation, and a citation that does not resolve is blocked before it reaches a user. A real passage can still be misread, which is why the span is shown for the lawyer to check.

On an on-premise or sovereign-cloud deployment, client litigation data has no egress path — it is not sent to public endpoints and is not used to train public models. That is a property of the network topology. Whether privilege is preserved in how a review is conducted is a legal question for your firm, and we design the deployment so it does not get in the way of that answer.

Review NDAs, MSAs and DPAs against your own playbook, flag clauses that deviate from your stated positions, extract change-of-control and liability provisions in a data room, and retrieve authority with a citation that resolves. The judgement stays with a lawyer — what changes is that they start from candidate issues with the evidence attached rather than from the whole document.

Yes. Purpose-built extraction pipelines process data rooms containing tens of thousands of PDFs, surfacing change-of-control provisions, liabilities and anomalous terms with a source citation on each finding. How much associate time that compresses depends on the data room; we measure it on a sample before quoting rather than promising hours in place of weeks.

Keep reading

Secure Data Pipelines

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