I build legaltech software on subscription: structured intake, matter management, document automation, AI-assisted drafting. Privilege-aware architecture. $4,999/mo.
- Scope
- Ship
- Iterate
monthly subscription
Who this is for
You're a legaltech founder, a firm-operations partner, or an in-house legal-ops lead. Your case management is either bloated with features you don't use or missing the ones your practice actually needs. Intake, billing, and documents are split across three or four tools. You've looked at AI drafting tools but your firm's data cannot go near a consumer model. Custom engineering is the answer — you just need a senior developer who understands why privilege protection is not a checkbox.
The pain today
- Intake data never flows cleanly into case management
- Document generation still means a paralegal copy-pasting from templates
- Off-the-shelf CMT either lacks your workflow or locks you into its structure
- AI tools your team tried treat customer matter data like any other input
- No single place to see matter status, billing exposure, and deadlines together
The outcome you get
- Intake-to-matter flow that runs without manual re-entry
- Document generation tied directly to structured matter data
- AI-assisted drafting with attorney review at every step — privilege intact
- Custom app that fits your practice area, not a generic legal platform
- Subscription at $4,999/mo, 2–4 day delivery cycles, cancel anytime
Where legal workflows break across tools
Legaltech web app development starts with a familiar problem: the tools don't talk to each other. Intake captures prospect details but stops at the intake form. Case management tracks the matter but not the documents. Documents live in a folder system without matter context. Billing pulls from time entries in a fourth tool that nobody updates consistently. Each handoff loses context and creates re-entry work — and in a practice where senior attorney time bills at several hundred dollars an hour, that friction is a real cost.
Firms under $10M in revenue often fix this with configuration: Clio, MyCase, or PracticePanther plus a few Zapier glues. That works for standard practice types. Custom engineering earns its cost when the firm has a workflow no vendor built for, when it's building a legaltech product itself, or when billing and matter logic is specific enough that vendor workarounds create more problems than they solve.
Intake-to-matter flow that runs without manual re-entry
Apps I build for legal practices and legaltech startups
Intake systems with conflict-check integration and routing to intake attorneys. Matter tracking that extends or replaces what the CMT provides. Document generation from structured templates tied to matter data — no copy-paste, no paralegal re-entry. Time entry capture, rate-card logic, and invoice generation for firms with billing rules a SaaS product won't support. Internal dashboards showing matter load per attorney, deadline exposure, and profitability by practice area.
For legaltech startups building a product rather than an internal tool, the scope broadens: multi-tenant architecture, subscription billing, customer-facing portals, audit trails for regulatory submissions. I've shipped fintech products at bolttech, a $1B+ unicorn, handling 40+ payment provider integrations across 15+ international markets — the same rigour around data isolation and audit logging that fintech demands translates directly to legaltech.
AI-assist features sit across both categories: document summarisation from long transcripts, first-draft generation from structured matter data, research support for finding relevant precedent. Each is scoped to where the time savings are real, not where it sounds impressive.
40+: Payment providers integrated.
Privilege-aware architecture for legal AI
The risk that courts have flagged — consumer AI tools used on active matters may compromise attorney-customer privilege entirely — is real and worth taking seriously at the architecture level, not just in policy.
I build legal AI integrations with three constraints. First, the LLM call happens in an enterprise tier with a data-processing agreement: OpenAI Enterprise or Anthropic's enterprise offering, where prompts are not used for model training and data is not retained beyond the request. Second, no document leaves the firm's environment without attorney review and explicit approval — the system treats AI output as a draft, always. Third, for firms with particularly sensitive matters, AI calls run against matter data that has been stripped of personally identifiable information before leaving the firm's infrastructure, with re-enrichment happening client-side after the draft returns.
This adds a few weeks to integration scope. It is worth it. A legaltech app that loses a firm's privilege protection is not a productivity tool — it's a liability.
Integrating with Clio, MyCase, and the tools you already use
Most firms have a CMT they are not replacing. The custom app needs to extend it, not fight it. Clio, MyCase, and PracticePanther all have well-documented APIs. Integration patterns I use: intake data flows to CMT on lead-to-customer conversion; matter updates sync bidirectionally when the custom app and CMT both own part of the workflow; billing flows from time entries in the custom app to invoice generation in CMT.
For firms preferring to keep CMT as the system of record — which is the right call when staff are already trained on it — the custom app wraps around it. For firms actively switching CMT, the custom app can become the system of record, but data migration complexity goes up sharply and needs to be scoped carefully before signing off.
DocuSign, Adobe Sign, and PandaDoc integrate cleanly for e-signature. For high-volume e-sign practices like estate planning or contract-heavy commercial work, envelope templates in DocuSign cut send time to seconds per document.
Pricing, data handling, and what the engagement covers
Standard plan is $4,999/mo. Pro is $5,499/mo. Both include 2–4 day delivery cycles, senior engineering only, and privilege-data awareness baked into the engagement agreement — I treat all customer data seen during development as privileged, full stop.
For firms not comfortable sharing real matter data during development, I work against synthetic data and run integration testing against production only after launch. This adds a week or two to the timeline but is standard practice for sensitive engagements.
14-day money-back guarantee. Cancel anytime. All code is yours under Work Made for Hire — no license fees, no lock-in, no dependency on me once you have the codebase. For firms with EU customers, GDPR data residency requirements factor into the hosting architecture from day one: AWS region selection handles most cases, with AWS GovCloud available for government-adjacent work.
When a vendor is the right answer
For litigation, family law, estate planning, and other standard practice types, Clio and its peers handle the majority of workflow needs. Configuration plus a few integrations covers most firms. I'm not the right fit for a firm that needs someone to configure a SaaS product.
Custom development makes sense when the practice area has workflow that vendors don't cover, when the firm is building a legaltech product to sell rather than use internally, or when integrations across systems are complex enough that maintenance of the glue layer becomes a full-time job. My target legaltech customers are founders building a product and legal-ops leads at firms where the workflow is genuinely unusual — specialty practices, multi-jurisdictional operations, or firms with billing logic that outgrew every CMT they've tried.
Recent proof
A comparable engagement, delivered and documented.
Unified payment orchestration across Asia and Europe
Delivered the payment orchestration platform at bolttech, a $1B+ unicorn, with 40+ integrations across multiple regions.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
Every AI integration I build for legal customers uses an enterprise-tier LLM contract — OpenAI Enterprise or Anthropic's enterprise offering — where data is not retained or used for model training. No document leaves the system without attorney review and approval. For highly sensitive matters, I strip personally identifiable information before any LLM call and re-enrich client-side after the draft returns. All of this is written into the engagement agreement, not left to policy.
Yes. All three have documented APIs. The integration pattern depends on what the firm wants to own: if the CMT stays as system of record, the custom app wraps around it and syncs data at key handoffs (intake conversion, billing export). If the firm is moving away from the CMT, the custom app can become the system of record — but data migration scope needs careful planning. I scope this in the first week before any code is written.
Structured intake systems with conflict-check logic, matter tracking dashboards for law firms that outgrew their CMT, document generation tied to matter data so nothing gets copy-pasted, AI-assisted drafting with human-in-the-loop review, and billing add-ons for firms with rate-card logic too specific for SaaS. For legaltech startups, I also build multi-tenant products with customer-facing portals, audit trails, and subscription billing.
For EU customers, GDPR data residency requirements factor into hosting architecture from day one — AWS region selection (eu-west-1, etc.) covers most cases. For US government-adjacent work or firms with court-mandated data residency, AWS GovCloud or dedicated infrastructure is available and I scope that separately. Data residency is not an afterthought added at launch — it shapes database design and environment configuration from the start.
A focused intake-to-matter flow or a document generation module typically ships in 8 to 14 weeks. Broader platforms — multi-tenant legaltech products, full CMT replacements, or apps with complex AI integrations — run 16 to 24 weeks. At bolttech, I delivered a payment orchestration system across 40+ providers without a single post-launch critical bug. Timelines hold when scope is locked; the first week of any engagement I spend on scope definition precisely to avoid surprises.
Useful in specific places: first-draft generation from structured matter data, summarising long documents or deposition transcripts, research support for finding relevant precedent, and flagging unusual clauses in contract review. Not useful for final legal judgement, complex novel matters, or anything customer-facing without attorney review. The firms I work with use AI to save senior attorney time on the deterministic parts — generation and summarisation — while keeping human judgement on the parts that actually require it.