Systems Built
Not demos.
Systems already put to work.
Different law firms. Different bottlenecks. Different architectures. Each system below was designed around real legal work, real decision points and real production constraints.
A useful automation story is not “we connected these apps.” It is what the firm stopped having to do after the system existed.
Production Case Files
Open a system. See what actually changed.
Each file opens into the operating problem, architecture, human control points, automated workload and production result.
01
Law Firm Operations
Attorney-Controlled Legal Service Engine
2–3h → 1–2m
Lease attorney handling
Attorney-Controlled Legal Service Engine
From fragmented legal operations
One governed service layer replaced a chain of manual handoffs.
An anonymized New Jersey landlord-tenant law firm connected paid consultations, residential lease drafting and non-payment eviction filing into one controlled operating system. The attorney remained responsible for representation, legal review and court action. The system took over the repeatable administrative chain.
A client event starts the workflow. Attorney decisions remain visible. Everything else moves through controlled conditions.
Attorney stays in control of
The system handles
Operating impact
Workflow-based operational estimatesClient identity and secure implementation details are redacted. Impact figures are case-study workflow estimates rather than audited financial results.
Explore Law Firm Operations
02
Document Automation
People-First Estate Planning Engine
4–5h → ~15m
Estate-plan preparation
People-First Estate Planning Engine
From six separate drafting tasks
One family data set now powers an interconnected estate plan.
An anonymized Pennsylvania estate-planning practice replaced repeated names, appointments and clause selection with a people-first Gavel architecture. Identities are entered once. Legal meaning is assigned through roles, rank and plan logic. The coordinated document package then draws from the same source.
The person is stored once. Relationships, appointments and document language are resolved downstream.
Legal decisions stay explicit
The drafting engine resolves
Preparation impact
Firm-reported prior process versus approximate Gavel intakeThe reported time comparison reflects the prior manual workflow and current approximate structured intake. Attorney review remains part of the legal service.
Explore Document Automation
03
AI Content Automation
Human-Controlled Legal Content OS
15–25 → 1–3
Manual actions to human clicks
Human-Controlled Legal Content OS
From scattered marketing tasks
Research, bilingual production, media and publishing became one governed pipeline.
An anonymized Texas immigration law firm connected manual content requests, an editorial calendar and automated news discovery to one production engine. AI handles research support, drafting and media preparation. Attorneys keep approval control before anything moves outward.
Different inputs are normalized into one structured record before AI, media, approval and publishing.
Human control remains mandatory
The production engine handles
Production workload
Workflow-based operational estimatesCounts reflect the visible as-built workflow evidence. Production remains human-controlled and the operational figures are not audited staffing results.
Explore Content AutomationOne Principle Across Every Build
The firm makes the judgment. The system carries the work.
The architectures are different because the legal work is different. What stays constant is the operating philosophy: structure the facts, make decision points explicit, automate the repeatable chain and design for what happens when the happy path breaks.