Contract triage, intake routing, and first-draft generation built for law firms and legaltech ops. Privilege-aware, attorney-supervised, audit-ready. $3,999/mo retainer.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
Legaltech founder, firm ops partner, or in-house legal-ops lead where contract review backs up weekly, customer intake is still a manual email chain, and senior attorneys spend hours on documents a structured prompt could draft in minutes.
The pain today
- Contract review backlog grows faster than headcount can absorb it
- Intake is inconsistent — same customer question gets three different routing decisions
- Document drafting pulls senior attorneys away from billable, judgment-heavy work
- Past AI experiments stalled over privilege exposure and ethics-rule anxiety
- Case management systems sit in silos — no AI layer connects intake to matter data
The outcome you get
- Contract triage with clause-level flagging, routed to the right reviewer
- Intake summarization matched to practice area and attorney workload
- First-draft generation for routine documents, ready for attorney sign-off
- Privilege-aware data architecture with no-training LLM agreements in place
- Prompt library your whole team builds on — institutional knowledge that compounds
Three legal-ops workflows that pay back quickly
AI automation for legaltech delivers the clearest ROI in three places.
Contract triage. The model reads incoming contracts, flags unusual clauses against your standard templates, scores risk by clause type, and routes to the appropriate reviewer. Firms that deploy this typically cut first-pass review time by 50 to 70 percent on routine agreements — SaaS, employment, NDAs — while senior attorneys stay focused on the deals that actually need their judgment.
Intake routing. A structured intake form feeds an AI layer that classifies matter type, practice area, urgency, and conflict markers, then routes to the right attorney queue. No more intake emails sitting in a shared inbox. The same information captured every time, in the same shape.
First-draft generation. Engagement letters, demand letters, standard motion responses — when the matter data is structured, the first draft writes itself and lands in an attorney review queue. I build the prompt templates from your real documents, so the output sounds like your firm, not a generic AI.
Contract triage with clause-level flagging, routed to the right reviewer
How privilege survives the AI layer
Attorney-customer privilege is the core asset of legal services, and the biggest reason firms hesitate on AI. The architecture decisions that protect it are not complicated, but they have to be intentional from the start.
Every LLM I connect to customer matter data carries a zero-training, no-retention enterprise agreement — Anthropic, OpenAI, and Azure OpenAI all offer these; consumer-grade accounts do not. For highest-sensitivity matters — M&A, active litigation, regulatory investigations — I scope self-hosted open-source models on firm-controlled infrastructure so privileged content never leaves the firm's network.
Prompts log privilege markers. Audit views redact privileged content for any non-attorney viewers. Access controls mirror the firm's existing customer-matter access structure. This isn't a policy document; it's the actual system design. Recent court decisions draw a sharp line between consumer AI use, which courts have found can waive privilege, and enterprise AI with documented governance, which preserves it. I build to the enterprise standard by default.
+500%: Lead base growth.
Integrating with the systems your firm already runs
The off-the-shelf legal AI vendors — Harvey, Ironclad, Luminance, LegalOn — solve specific tasks well. They do not integrate deeply with your existing case management system, billing platform, or internal document library. That gap is where custom AI automation earns its place.
I've connected AI layers to Clio, Filevine, and MyCase via their APIs — pulling matter data into prompt context so drafts reference actual case facts, intake routing triggers real matter creation, and triage output writes back to the matter timeline. For document management, AI output goes into NetDocuments or iManage with version control intact. For billing, automated summaries feed time-entry drafts.
These integrations are what transform AI from a side tool into a workflow. I build them once, document them, and hand them off with full code ownership. Your team runs them. They don't depend on me staying engaged.
Ethics compliance built in, not bolted on
Most state bars now have formal guidance on attorney AI use. The common threads: attorney supervision of AI output, prohibition on unsupervised AI legal advice, and in some jurisdictions disclosure to customers when AI plays a material role.
I build workflows that match the strictest reasonable interpretation. Attorneys review AI output before it goes anywhere. Customers are informed where appropriate. The firm documents its AI policies and maintains a list of approved tools and configurations. For multi-jurisdictional practices, the strictest applicable rules set the floor.
Human-in-the-loop is not an add-on for compliance theater. It's the actual design. AI drafts never exit the firm without attorney review. For routine documents that review may take two minutes. For substantive work it may take two hours. Either way, the attorney signs off and the accountability chain is intact.
Building an institutional prompt library
The firms that get the most from AI automation are not the ones with the most sophisticated models. They're the ones with the best prompt libraries.
I built Instill as a structured AI skills platform — 30+ active users, 1,000+ skills saved, 45+ projects powered. The same pattern applies directly to legal. Your firm accumulates template prompts for engagement letters, demand letters, common motion responses, contract clause comparisons, and intake classification. That library becomes institutional knowledge: junior attorneys use it, senior attorneys maintain it, and quality compounds with every real matter.
The prompt library also solves the consistency problem. When ten attorneys handle intake, intake is inconsistent. When ten attorneys all run the same intake prompt against their own judgment, the structured output is consistent and the judgment part stays human.
Pricing and what the retainer covers
The AI automation retainer is $3,999/mo. That covers scoping, integration engineering, privilege-aware architecture design, prompt engineering, monitoring, and iteration throughout the engagement. LLM API costs pass through at cost — no markup. 14-day money-back guarantee. Cancel anytime after that.
100 percent code ownership under Work Made for Hire. NDA standard, with customer data treated as privileged within the engagement. For firms requiring self-hosted infrastructure for highest-sensitivity matter types, that scope and the associated infrastructure costs extend the base engagement.
For specific legal tasks with established vendor solutions — Westlaw AI for research, Harvey for brief generation, ContractPodAi for CLM at scale — I'll say so directly rather than compete with a tool that does one thing better than custom code. The retainer pays back when you need custom practice-area templates, deep integration with existing systems, or infrastructure you own.
Recent proof
A comparable engagement, delivered and documented.
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Read the case studyFrequently asked questions
The questions prospects ask before they book.
Consumer-grade AI use — free ChatGPT, unmanaged Claude accounts — has led courts to find privilege waived in at least one recent case. Enterprise AI with zero-training agreements, documented governance, and attorney supervision does not automatically waive privilege. The key variables are the tool's data handling terms, whether input is used for model training, and whether an attorney meaningfully reviews the output. I build to the enterprise standard: no-training enterprise agreements, audit logging, and attorney review at every output stage.
For legal research: Westlaw AI and Lexis+. For contract review and CLM: Ironclad, LegalOn, Luminance, ContractPodAi. For brief and motion drafting: Harvey, Thomson Reuters CoCounsel. For intake and workflow routing: custom integrations built on top of case management systems like Clio or Filevine. Most firms use a combination — specialist vendors for their core tasks, custom automation for practice-area-specific workflows and system integrations those vendors do not cover.
Intake routing is one of the highest-ROI applications in legal ops. AI classifies matter type, practice area, urgency, and potential conflicts from a structured intake form, then routes to the right attorney queue. It does not replace the attorney's initial customer consultation or conflict check — those stay human. What it replaces is the shared inbox where intake emails sit for hours, and the inconsistency that comes from different staff reading the same intake differently.
Three controls matter most: (1) Only use LLMs with enterprise zero-training, no-retention agreements — not consumer accounts. (2) For your most sensitive matter types, self-hosted open-source models keep data on firm-controlled infrastructure. (3) Access controls on AI tools should mirror your existing customer-matter access structure, so paralegals cannot see matter data they could not access in your case management system either. Written AI policy, approved-tool lists, and updated engagement letter language round out the governance layer.
For routine contracts — standard SaaS agreements, NDAs, employment agreements, form leases — AI triage plus clause-level flagging typically reduces first-pass attorney review time by 50 to 70 percent. For complex transaction documents, AI handles first-pass review but senior attorneys still do the substantive work. The math is straightforward: if a senior associate spends four hours a week on routine contract triage, automating that recovers roughly 200 billable hours a year.
Harvey and Ironclad are excellent tools for their core tasks — brief drafting and CLM respectively. They do not integrate deeply with your specific case management system, billing platform, or internal document templates. Custom automation fills that gap: intake triggers that create matters in Clio, draft outputs that reference your actual precedent library, prompt templates built from your real documents rather than generic legal text. If an off-the-shelf tool covers your need, I'll say so. Custom work earns its place in the gaps.