Privilege-aware AI automation for law firms and legaltech
Contract triage, intake summarisation, draft generation with human-in-the-loop and attorney-client privilege protections. $3,000/mo retainer.
Who this is for
Legaltech founder, firm ops partner, or in-house legal-ops lead where contract review is slow, intake is manual, and document drafting consumes senior time.
The pain today
- Contract review backlog grows weekly
- Intake is manual and inconsistent across attorneys
- Document drafting consumes senior attorney time
- Legal research is slow and expensive
- Previous AI experiments worried about privilege and ethics
The outcome you get
- AI automations for legal ops on $3,000/mo retainer
- Contract triage with clause-level flagging
- Intake summarisation routed to appropriate practice area
- First-draft generation for attorney review and finalisation
- Privilege-aware architecture with appropriate data handling
Practical legal-ops AI wins in 2026
Three places deliver clear ROI. Contract triage — LLM reads contracts, flags unusual clauses, compares to standard templates, routes to appropriate reviewer. Cuts triage time 60 to 80 percent. Intake summarisation — prospect intake structured into case type, practice area, timeline, and routed to correct attorney. First-draft generation — routine documents (engagement letters, demand letters, standard responses) drafted from structured matter data for attorney review. In each, AI removes typing time while attorneys keep judgement.
Privilege and data-boundary architecture
Attorney-client privilege is the core asset of legal services. AI design rules. Privileged content only goes to LLMs with no-training terms and data-processing agreements that preserve privilege. For highest-sensitivity matters, self-hosted open-source models on firm-controlled infrastructure. Prompts log privilege markers; shared audit views redact privileged content. Access controls match firm's attorney-staff structure. Violating privilege creates malpractice exposure and reputational damage — architecture prevents violations by default.
Human-in-the-loop for drafts
AI drafts never leave the firm without attorney review. For routine documents, review may be quick (engagement letters, form responses). For substantive documents (briefs, contracts, complex letters), attorney reviews, edits, and signs off. AI is a typing assistant that happens to understand legal language — not a legal decision-maker. Ethics rules in most jurisdictions require attorney supervision of AI-generated work product; building to this rule from day one keeps the firm safe.
Pricing and engagement model
$3,000/mo retainer. Covers AI integration, privilege-aware architecture, prompt engineering, monitoring, iteration. 14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. NDA standard (treating client data as privileged in engagement). LLM costs pass through. For firms with strict data sensitivity, self-hosted infrastructure scope and cost extend base engagement.
Case: Instill — structured prompt library for legal templates
I built Instill as a self-initiated AI skills platform. Current state: 30+ active users, 1,000+ skills saved, 45+ projects powered. Stack: Next.js 16, React 19, TypeScript, PostgreSQL, Vercel, MCP Protocol. For legal, the structured-prompt pattern is high-value. Firms accumulate template prompts for engagement letters, demand letters, common motion responses, contract reviews. Library becomes institutional knowledge that junior attorneys use and senior attorneys maintain. Quality compounds with every real case.
When a legal-AI vendor covers it
For specific legal tasks, specialist vendors are well-established. Contract AI: ContractPodAi, Ironclad, Luminance. Legal research: Westlaw AI, Lexis+. Brief generation: Harvey, Lexis Protégé. For firms with budget and standard needs, these specialist tools often outperform generic AI. My retainer pays back when firms have specific integration needs (custom practice areas, firm-specific templates, integration with case management) or want to own the AI infrastructure. Many firms use specialist vendors for specific tasks plus my retainer for custom work.
Recent proof
A comparable engagement, delivered and documented.
A prompt library that works with every AI tool
A home for your best AI prompts. Save them once, then use them in Claude, Cursor, or any AI tool you work with. No more copy-paste.
Frequently asked questions
The questions prospects ask before they book.
- How do you protect privilege with AI?
- LLM providers with no-training commitments and data-processing agreements that preserve privilege. For highest-sensitivity matters, self-hosted open-source models on firm-controlled infrastructure. Prompts categorised by privilege level; shared audit views redact privileged content. Access controls per attorney match firm's existing client-matter access structure. Documented in firm's ethics compliance posture. For firms in jurisdictions with formal ethics opinions on AI use, specific guidance captured in architecture.
- What about data residency?
- For firms with clients requiring specific data residency (EU clients under GDPR, specific industry clients), AI infrastructure runs in-region. Azure OpenAI with EU region, AWS Bedrock with EU regions, self-hosted open-source on EU-based infrastructure. US firms without residency constraints can use US-based infrastructure. Residency decisions per client matter during onboarding.
- Which LLM for legal work?
- Anthropic Claude excels at long-document analysis, nuanced legal reasoning, and drafting — the primary choice for most legal AI. OpenAI for structured extraction and function-calling integrations. For data that cannot leave firm infrastructure, self-hosted open-source (Llama via vLLM). Anthropic and OpenAI both have enterprise tiers with no-training terms. Model choice per task, not per brand preference.
- Can AI really help with contract review?
- For routine contract review (standard clauses, typical SaaS agreements, common employment contracts), AI triage plus clause-level flagging saves 50 to 80 percent of attorney time. For complex transaction documents (M&A, structured finance, bespoke agreements), AI helps with first-pass review but senior attorneys still do the substantive work. Rule of thumb: AI handles contracts that look similar to many others in the firm's library; humans handle contracts that are unique.
- What about ethics compliance?
- Most state bars have issued guidance on AI use by attorneys — typically requiring attorney supervision, prohibition on unsupervised AI legal advice, and disclosure to clients in some contexts. I build AI workflows that match the strictest reasonable interpretation of these rules. Attorneys review AI output. Clients know AI is used where material. Firm documents AI policies. For firms in multi-jurisdictional practice, the strictest applicable state rules apply.
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