LEGAL WORKFLOW DEMONSTRATION

Governed AI assistance without handing over legal judgment.

Star Ridge Intake is a working demonstration of how an unstructured legal inquiry can become structured, reviewable work while preserving uncertainty and human control.

What the demonstration proves

  • Structured extraction from unstructured inquiries
  • Evidence first handling of reported facts
  • Unknown information remains unknown
  • Missing information is surfaced for follow up
  • Controlled follow up generation
  • Human review before consequential downstream action
  • Persistent audit history
  • Automated regression and live model evaluation
Commercial status: This is a tested demonstration, not a claim of an existing production law firm deployment. Customer implementations would be assessed, scoped, secured and validated for that firm's actual systems and workflow.
Star Ridge Systems workflow automation mark
▦ Intake queue
✓ Review
⌘ Routing
≡ Audit

Evidence & explainability

ReportedSource supported client facts
UnconfirmedMissing or ambiguous information
WorkflowControlled routing and human review signals
MOUNTAIN VIEW FAMILY LAW · SYNTHETIC CASE

From workflow discovery to an automation recommendation ready for a decision.

Mountain View Family Law is a synthetic firm with 12 people created to exercise the Star Ridge assessment process. It demonstrates methodology and software capability; it is not represented as a customer or production deployment.

4Stakeholders modeled
8Workflow steps
5Evidence items
89 / 100Top opportunity score

Opportunity

Automate prospective client intake data capture and case system entry to reduce duplicate entry, incomplete information and administrative handling.

Human control boundary

Conflict review, case acceptance, legal conclusions, legal advice and other professional decisions remain with attorneys and authorized staff.

What this demonstrates: Star Ridge can turn discovery evidence into pain points, scored opportunities, business case inputs, risk and assumption controls, an implementation roadmap and a recommendation suitable for commercial scoping.

Not an opaque chatbot.

The engineering goal is controlled workflow automation: structured outputs, validation, auditability, explicit human boundaries and measurable behavior.