AI for law firms and professional services that keeps client confidences intact
AI for law firms has to protect privilege, client confidences and professional judgment. We design matter-aware AI platforms, knowledge systems and governance for law firms, legal departments and advisory firms that clients and ethics counsel accept.
What is different about AI in legal and professional services
AI for law firms works with the most confidential material a client has, under duties that belong personally to the professionals involved. Confidentiality, privilege and competence are not just policies; they are ethical obligations enforced by bars and courts. A tool that mixes one client's documents into another client's answer is not a bug report, it is a potential breach.
The work is also fundamentally about language and documents: research, drafting, review, diligence and knowledge reuse. That makes generative AI unusually valuable and unusually risky. Hallucinated citations have already led to sanctions in several courts, and many judges now issue standing orders on AI use in filings.
Professional services firms in accounting, consulting and advisory face a parallel set of issues: client confidentiality clauses, independence rules and engagement terms that restrict how client data may be used. In every case the answer is architecture that keeps each client's information inside its own boundary and keeps a qualified professional accountable for what leaves the firm.
Where AI earns its place
Matter-aware knowledge search
Search the firm's precedents, briefs, memos and deal documents with answers that cite sources, filtered by matter access and ethical walls. Lawyers find the firm's best prior work in minutes, and restricted matters never appear for people outside them.
Contract review and diligence
Extract key terms, flag deviations from playbooks and summarize risk across large document sets in transactions and diligence. Lawyers review every flag, and extraction accuracy is measured against lawyer-reviewed samples for each document type.
Drafting from firm precedent
Produce first drafts of agreements, letters and memos from approved templates and clause libraries, with citations to the precedent used. Drafts start from the firm's standards rather than a model's general training, and a lawyer owns the final work product.
Research assistance with verified citations
Summarize research results and draft analysis using authoritative legal research sources, with automated citation checking against those sources before anything reaches a lawyer. Unverifiable citations are flagged, not silently included.
Litigation and discovery support
Summarize depositions, build chronologies and assist first-pass review within the review platform, under defensible, documented protocols. Privilege screening is flagged for lawyer decision, and the workflow records how AI was used in case that use must be explained.
Engagement and practice operations
Draft engagement letters, summarize time entries into invoice narratives, and answer internal policy and conflicts process questions. Lower-risk operational use cases that build firm-wide familiarity and prove the platform before client-facing work.
The professional and regulatory constraints we design for
Legal and professional services AI is governed mainly by professional duties and client contracts. These are the constraints that shape our designs most.
- ABA Formal Opinion 512
- Issued in July 2024, ABA Formal Opinion 512 addresses lawyers' use of generative AI under duties of competence, confidentiality, communication, supervision, candor and reasonable fees. State bars have issued their own guidance, which varies, so firms should check each jurisdiction where they practice.
- Client confidentiality
- Model Rule 1.6 and its state equivalents require reasonable efforts to prevent unauthorized disclosure of client information. That drives provider terms that prohibit training and retention, private deployment options, and a clear view of every system client data passes through.
- Attorney-client privilege and work product
- Disclosure to third parties can put privilege at risk. We design so privileged material is processed only by providers acting as the firm's agents under confidentiality obligations, and so prompts and outputs are treated as work product with appropriate access and retention.
- Ethical walls and conflicts
- Screens that separate lawyers from matters must hold inside AI systems too. Retrieval, fine-tuning and shared indexes must respect wall membership, or the AI becomes a way around the screen. Access is inherited from the document management system and matter intake.
- Client outside counsel guidelines
- Many corporate clients now include AI terms in outside counsel guidelines or engagement letters, from notice requirements to outright restrictions. We design per-client policy flags so the platform can enforce each client's instructions automatically.
- Court rules and candor to the tribunal
- Some courts require disclosure or certification of AI use in filings, and every court expects accurate citations. Workflows must verify citations against authoritative sources and record where AI assisted, so lawyers can meet those obligations with confidence.
How we architect AI for law firms and professional services firms
The matter is the unit of security. We connect AI to the document management system, matter intake and wall management so that every retrieval, prompt and output is scoped to matters the user can already access. Shared firm knowledge is curated and cleared for reuse separately from live client files.
A gateway sits between all users and all models. It enforces approved providers, applies client-specific restrictions from outside counsel guidelines, strips or masks data where required and logs usage by matter. That log supports client inquiries, billing transparency and incident response if something goes wrong.
We use buy and build together. Specialized legal AI products are often the right answer for research and review, while firm-specific knowledge search and drafting from precedent frequently justify a firm-owned platform. We help firms decide where each fits and design integration so commercial tools respect the same matter and wall controls.
Evaluation is done by lawyers. We build test sets from real, cleared work product, score accuracy, completeness and citation validity, and monitor after launch. That evidence gives general counsel and risk committees a concrete basis for approving AI for law firms and their clients' work.
AI in legal and professional services: common questions
Is using generative AI a breach of attorney-client confidentiality?
Using generative AI is not inherently a breach of confidentiality, but lawyers must take reasonable steps to protect client information. That means using tools whose providers do not train on or retain your data, understanding where data goes, controlling access by matter and, in some situations, getting informed client consent. ABA Formal Opinion 512 discusses when consent may be needed.
What does ABA Formal Opinion 512 say about AI?
ABA Formal Opinion 512, issued in July 2024, says lawyers using generative AI must understand its capabilities and limits, protect client confidentiality, communicate with clients about AI use when appropriate, supervise AI-assisted work, verify outputs before relying on them and charge reasonable fees. It applies existing Model Rules rather than creating new ones.
How do law firms prevent AI hallucinations in legal work?
Law firms prevent AI hallucinations by grounding outputs in authoritative sources, automatically verifying every citation against legal research databases, showing lawyers the source for each statement and requiring lawyer review before anything is filed or sent. Tools that generate citations from model memory alone should not be used for research or court filings.
Should a law firm build its own AI platform or buy legal AI tools?
Most firms should do both. Commercial legal AI tools are strong for research, contract review and discovery. A firm-owned platform makes sense for search across the firm's own precedent and knowledge, drafting from firm templates and enforcing firm-wide controls. The key is one set of matter, wall and client restriction controls across everything.
How do ethical walls work with AI systems?
Ethical walls work with AI systems when the AI inherits access from the same sources that enforce the wall, usually the document management system and conflicts database. Retrieval queries filter by the user's permitted matters at query time, and shared indexes or fine-tuned models must never mix walled content, or the AI becomes a path around the screen.
Other industries
- Financial Services
- Insurance
- Healthcare and Life Sciences
- Public Sector
- Manufacturing
- Technology and SaaS
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