Problem
Legal intake is high-friction for callers and high-risk for firms when urgency, jurisdiction, conflict checks, and advice boundaries are handled inconsistently.
Project case study
RoboReceptionist
AI-assisted legal intake system that guides non-experts through complex legal situations with structured workflows and knowledge retrieval.
Project facts
Theme
Applied AI & Automation Systems
Current state
Prototype
My role
Sole architect and backend engineer
Core constraint
Validation and safety boundary: every LLM response must pass through a deterministic policy engine before reaching callers
Outcome
Working prototype with policy-gated intake flow, jurisdiction detection, and conflict-check pipeline
Approach
A concise problem → system → evidence structure so the engineering story is easier to inspect.
System design
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.
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.
Stack
FastAPIPolicy EngineLLM ValidationSQLite / PostgresEmail Notifications
Evidence
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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