AI for legal teams with privilege enforced at the index
Contract review against your playbooks, due diligence document rooms, matter research over precedents and intake support, for law firms, in-house teams and legal-tech companies, designed so confidentiality is a property of the system rather than a promise in the prompt.
Reading at volume, comparing against a standard and finding the precedent you wrote three years ago: this is where lawyers lose hours and AI gives them back.
Contract review against playbooks
Clauses extracted, compared with your preferred and fallback positions, deviations highlighted and a first-draft markup produced for the lawyer to edit. Touches the document management system and the contract lifecycle tool. Best on high-volume agreement types.
Matter research over precedents and know-how
Ask a question, get an answer grounded in the firm's own precedents, notes and past advice, with citations, limited to what the asker may see. Touches the DMS and the knowledge management system. See enterprise RAG.
Due diligence document rooms
Thousands of data-room documents classified, key terms extracted into a schedule and red flags surfaced for the deal team. Touches the virtual data room and the review platform. Reviewers verify; the schedule assembles itself.
Intake, triage and conflict-check support
New enquiries summarised, categorised and checked against existing parties for a conflicts analyst to confirm. Touches the practice management system and the CRM. Faster first response without skipping the check.
Time narratives and matter summaries
Draft time entries from calendar, email and document activity, and plain-language matter status updates for clients, approved by the fee earner. Touches the practice management and billing system. Fewer unbilled hours and clearer updates.
Disclosure and e-discovery triage
Semantic search and classification across large document sets to find the relevant, the privileged and the duplicate before human review. Touches the e-discovery platform. See semantic search.
Compliance and data
Privilege, confidentiality and professional responsibility
What we design for in legal
Client confidentiality and legal professional privilege are absolute requirements, so the system must know which documents are privileged, who may see each matter and where processing happens. Ethical walls between matters must hold in the retrieval layer. Professional rules on competence and supervision mean a lawyer reviews and owns every output. Client engagement terms may restrict third-party processing, and retention and destruction schedules apply to indexes as well as documents. This is engineering guidance, not legal advice; your risk and compliance partners own the policy.
We build on your DMS permissions rather than copying documents into a separate store, tag privileged material at ingestion, offer private deployment of open-weight models and log every retrieval and generation. The AI governance service produces the policy and the LLM security audit tests the boundaries before launch.
Reference architecture
Flagship workflow: contract review with matter-level access control
Documents stay in the DMS. An index is built per matter that mirrors the DMS permissions, so the retrieval layer can only return what the requesting lawyer is entitled to see. Privileged documents carry a tag that any wider-audience assistant excludes.
The review assistant extracts clauses, compares them with the firm's playbook and precedents, drafts a markup and explains each deviation with a citation. The lawyer reviews, edits and signs the output. Nothing is filed without them.
The approved markup and the review notes are written back to the DMS, and an audit record captures who accessed which matter and what was generated. The whole pipeline can run inside your environment; see on-prem versus cloud LLMs for the trade-offs.
Access control lives in the index and the audit lives outside the model, so confidentiality does not depend on a prompt.
Track record
What we have built that applies
Multi-tenant isolation in production
Eventify and ZekoHR both serve many organisations from shared infrastructure without leakage between them. Matter-level isolation is the same discipline at a finer grain.
No. We use models under contracts that exclude training on your data, or deploy open-weight models inside your environment where nothing leaves. Retrieval indexes are built per matter with the same access rules as your document management system. See secure RAG architecture.
How are ethical walls and privilege respected?
At the index, not in the prompt. A lawyer can only retrieve from matters they have access to in the DMS, because the retrieval layer checks the same permissions on every query. Privileged material is tagged and can be excluded from any assistant that serves a wider audience. Every access is logged.
Will the assistant hallucinate case law or clauses?
A grounded assistant answers only from the documents it retrieves and cites them; where nothing relevant exists it says so. We do not build assistants that generate legal authority from memory, and we evaluate on your own documents before launch. See evaluating LLM outputs.
Where do most firms start?
Contract review against a playbook for a high-volume agreement type, or a research assistant over the firm's own precedents and know-how. Both are measurable in hours and both keep the lawyer as the author.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.