For Legal Professionals
ABA Model Rule 1.6 doesn't carve out exceptions for ChatGPT. We build private AI that knows your firm — without ever touching a public LLM.
What's at Stake
Legal AI isn't just a data governance question — it's a professional responsibility question. These are the rules your firm is already bound by.
Attorneys have a duty of confidentiality to clients. Sending privileged communications to a third-party AI service may constitute unauthorized disclosure — and the ABA hasn't carved out an exception for productivity tools.
The ABA formally addressed generative AI use by attorneys. Opinion 512 requires lawyers to understand how AI tools handle client data, obtain informed consent where necessary, and verify AI-generated work product. Public LLMs fail on point one.
Sharing privileged communications with a third-party service may waive privilege. Courts are still sorting out whether AI tool disclosures constitute "third-party" waiver. You don't want to be the test case.
Most firms don't realize their conflict-checking data — matter histories, client names, opposing parties — is some of the most sensitive data in the firm. Exposing it to a public model creates lateral hire and privilege risks that compound over time.
The Problem
Every time an associate pastes case strategy into ChatGPT, they may have just shared privileged communications with a third-party model. ABA Model Rule 1.6 doesn't carve out exceptions for generative AI.
Lateral hires carry emails and matter files from prior firms. A junior associate querying an AI trained on your client list could be surfacing data from opposing parties' counsel.
Most conflict systems don't surface patterns across matter histories the way an AI that knows your full file archive would. False negatives create real malpractice exposure.
What It Does
Run NDAs, MSAs, and lease agreements through a model trained on your firm's preferred positions. Associates get faster redlines, partners get consistent outcomes.
Query years of prior matters, briefs, and deposition transcripts. The model knows your precedents — not generic case law from the internet.
Qualify inbound matters faster. Pull conflict flags, surface similar matters in your history, and route the intake to the right partner before the client even hangs up.
How We'd Approach It
Four phases. Fixed-price. You own the model at the end. See the full methodology →
We inventory your matter files, email archives, and document management system. We map what's privileged, what's confidential work product, and what can be used for fine-tuning. You review and approve the training corpus before a single document enters the pipeline.
We stand up a VPC-isolated deployment — your cloud account or on-premise. The model never routes through public APIs. Role-based access controls ensure associates see their matters, not others'. Conflict-checking logic is built into the query layer.
We fine-tune on your firm's documents and validate against your preferred contract positions, brief formats, and research patterns. Partners and associates review outputs before go-live. We iterate until it's right.
Model weights and deployment config transfer to you. Monthly retainer keeps the model current as caseloads shift and new matter types come in. You own it — we maintain it.
Sample Work Product
See the depth of a Vermont AI Systems engagement — a complete AI Readiness Assessment in Law Firms format.
See a sample AI Readiness Assessment for a Vermont law firm →Interactive Demo
See exactly what private AI feels like for a 52-attorney Vermont law firm. Ask about M&A contracts, litigation strategy, billing history, and partner workload — all sourced from fictional internal documents.
Try the Green Mountain Legal Partners demo →Common Questions
Yes — when properly structured. ABA Formal Opinion 512 permits AI use when attorneys understand how the tool handles client data, take reasonable precautions, and supervise AI-generated output. A private deployment you control satisfies those requirements. A public LLM you don't control creates material professional responsibility risk.
Yes. We integrate with most DMS and matter management platforms during the data pipeline phase. Conflict-checking queries run against your firm's actual matter history — not a third-party database. No client names or matter data leave your environment.
You keep it. Model weights, fine-tuning data, and deployment configuration transfer to you at project close. The retainer covers ongoing maintenance and retraining — not access. If you end the relationship, you retain a fully functional model. We don't believe in lock-in.
More questions? See the 15 questions to ask any AI vendor →
The discovery call is 30 minutes. We'll tell you exactly what it would take to build this for your organization, what it would cost, and whether we're the right fit.