Matter research
Querying across prior work product, memos, and precedent the firm already owns, with citations to the source document and matter.
AI for Legal & Professional Services
Retrieval across matter files that preserves privilege and respects the ethical walls you already maintain.
The constraint
Privilege is not a permission setting
Law firms hold some of the most sensitive material in commercial life, under duties that are professional rather than merely contractual.
Privilege can be waived by disclosure. Sending privileged material to a third-party processor raises a question most firms will not risk answering in front of a court. That eliminates commercial APIs for the work where AI would be most valuable.
Ethical walls are matter-level and absolute. A retrieval system that aggregates across the firm will surface material across a wall, which is a conflict event rather than a bug. Entitlements must be enforced at query time against the same matter-level rules your DMS already applies.
Client outside counsel guidelines increasingly speak to AI directly, and often require disclosure, restrict tooling, or prohibit third-party processing outright. Those obligations belong in the architecture, not in a policy document.
Applications
Where AI creates value in a practice
Document review
First-pass classification and issue spotting at volume, with every call traceable to the passage that drove it.
Deposition and discovery analysis
Cross-referencing testimony against the record to surface inconsistencies and gaps far faster than linear review.
Knowledge management
Turning decades of accumulated work product into something answerable, which is the asset most firms hold and cannot use.
Conflicts and intake support
Surfacing potential conflicts from unstructured matter history that structured conflict searches miss.
Compliance
Professional obligations
| Framework | What it drives in the architecture |
|---|---|
| ABA Model Rule 1.6 | Confidentiality of client information. Drives the prohibition on third-party processing of privileged material without informed consent. |
| ABA Model Rule 1.1 (comment 8) | Technology competence. Increasingly read to require understanding of the tools used, including their failure modes. |
| Outside counsel guidelines | Client-specific and frequently now AI-specific. Some require disclosure, some prohibit particular tooling outright. |
| Ethical walls | Matter-level access restrictions must be enforced by the retrieval layer at query time, not assumed from index-time permissions. |
| Data residency | Cross-border matters can carry jurisdiction-specific requirements on where material may be processed. |
We align AI governance to NIST AI RMF and ISO/IEC 42001 alongside your sector-specific obligations.
Integration
Integration with practice systems
We integrate with iManage and NetDocuments for document management, Relativity for e-discovery, and practice management systems including Elite. Retrieval runs under the requesting lawyer's existing matter entitlements, so ethical walls are enforced by the same source of truth the DMS uses — a user cannot reach through AI what they could not open directly.
Questions
Frequently asked
Does using AI risk waiving privilege?
The risk comes from disclosure to a third party. On a private deployment there is no third-party processor in the path — material never leaves the firm's infrastructure, so the disclosure question does not arise.
How are ethical walls enforced?
At query time, against your DMS entitlements. Filtering after retrieval is not sufficient, because the material has already been aggregated. The wall has to sit in front of retrieval, not behind it.
Can we tell clients we do not send their material to AI vendors?
On a private deployment, yes — and you can show the architecture that makes it true. That is increasingly a competitive advantage in outside counsel guideline negotiations rather than merely a defensive position.
Related
Private AI
Production AI systems running entirely inside your infrastructure, with no third-party processor in the data path.
Private AIDeployment Models
Managed API, private cloud, on-premise, edge, and air-gapped compared — including where each one fails.
Compare modelsDiscuss your constraints.
The assessment evaluates your data, infrastructure, and regulatory position, then recommends a deployment architecture your review process can actually approve.
We will sign your NDA before a detailed technical discussion.