Private AI for Law Firms

Rule 1.6(c) requires lawyers to make reasonable efforts to prevent the unauthorized disclosure of client information — it does not prohibit the use of AI. ABA Formal Opinion 512 (July 2024) and North Carolina 2024 FEO 1 (November 2024) both confirm that AI use is permitted under the existing duties of competence, confidentiality, and supervision. What the rules impose is a diligence burden: a firm has to understand where client data goes, who holds it, and whether it trains someone else's model.

That burden is exactly what a private deployment collapses. When the AI runs inside the firm's own environment, the confidentiality analysis shifts from vetting an outside vendor's data practices to applying the firm's own controls — a question firms already know how to answer. For the full breakdown of the rule, the opinions, and the reasonable-efforts test, read our deep dive on Rule 1.6(c) and law firm AI confidentiality.

The difference is the environment: Deepvine runs in your infrastructure instead of a shared public cloud. General assistants like ChatGPT and Copilot are powerful, but they were built for the open internet, not for a firm that has to account for where every client document travels.

Deepvine connects to your document management system, email, and Slack, and it is backfilled with your firm's own matter history so its answers come from your work product rather than the public web. Your data stays in your control, and your inputs are never used to train models that other organizations share. The result is an assistant that actually knows your matters, your precedents, and your institutional history — not a generic one you have to keep re-explaining your practice to.

Attorneys use Deepvine to find and reuse the firm's own knowledge without hunting through folders or emailing colleagues who may have left. Because the system is grounded in the firm's actual documents, every answer points back to its source.

• Precedent and prior-work retrievalsurface the clause, brief, or memo the firm has already written for a similar matter.
• Institutional knowledge from departed partnersrecover the reasoning and history that used to live only in one person's head.
• Conflicts contextpull the background a lawyer needs to understand a party or prior engagement quickly.
• Grounded draftingdraft briefs and memos anchored in the firm's own work product rather than generic language.
• Source-cited answers in Slackask a question where the team already works and get an answer with citations back to the underlying documents.

Deployment is a managed process, not a weekend software install. It moves through four stages: connection, backfill, daily diffs, and ongoing maintenance.

First we connect Deepvine to your existing systems. Then we backfill your historical matters so the assistant starts with real institutional memory instead of a blank slate. From there, daily diffs keep the knowledge base current as new documents and messages are created, and we handle managed maintenance so your team is not responsible for operating the system. To see the mechanics in more detail, visit how it works and our security overview.

1. 1ConnectWe connect Deepvine to your existing systems.
2. 2BackfillHistorical matters load so it starts with real institutional memory.
3. 3Daily diffsNew documents and messages keep the knowledge base current.
4. 4Managed maintenanceWe operate the system so your team does not have to.

Deepvine is built for firms of roughly 10 to 75 attorneys in document-dense practices where institutional memory is concentrated in a handful of people. That is the range where a firm has enough accumulated work product to make retrieval genuinely valuable, but not so much internal engineering that it would build this itself.

If your practice produces a heavy volume of documents, relies on precedent, and would feel real pain if a senior partner retired tomorrow, this is built for you. You can read more about who we are and why we build this way on our about page.