Problem
Legal intake is high-friction for callers and high-risk for firms when urgency, jurisdiction, conflict checks, and advice boundaries are handled inconsistently.
R&D case study
RoboReceptionist
Legal intake workflow that screens urgency, gathers structured information, and routes cases without inconsistent or unsafe responses.
At a glance
Delivery stage
R&D
Current state
Prototype
My role
Sole architect and backend engineer
What was built
Built as a guarded intake architecture. A deterministic policy engine enforces jurisdiction, emergency, conflict, and legal-advice constraints before an AI layer can respond. Validated outputs are persisted with transcripts and routed to intake specialists through notification workflows.
Result
Working prototype with policy-gated intake flow, jurisdiction detection, and conflict-check pipeline
How the system was structured
This section shows the operational logic behind the build, not just the user-facing surface.
Key system pieces
Policy engine gates every interaction before LLM output can be returned.
State-driven intake flow keeps conflict checks and urgency triage early.
Transcript persistence and notifications keep the system auditable.
Core constraint
Validation and safety boundary: every LLM response must pass through a deterministic policy engine before reaching callers
Stack
FastAPIPolicy EngineLLM ValidationSQLite / PostgresEmail Notifications
Supporting proof
The system work is visible in the intake flow design, safety boundaries, and validation-first response architecture.
Architecture diagramIntake state flowValidation boundary
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