What the system had to solve
Legal teams need more than a chat interface. They need a system that can ingest complex material, preserve the relationship between claims and sources, support organization-level access, and make generated reports auditable. The engineering challenge was therefore not only model integration; it was the design of trustworthy boundaries around AI output.
Architecture at a glance
Open interactive architecture ↗
Module structure
Document organization, extracted passages, source-linked review, and the context needed to inspect an AI-assisted result.
Multi-agent processing with explicit execution state, specialized steps, and boundaries between ingestion, analysis, verification, and reporting.
Validation gates designed to check that claims and generated passages remain traceable to source material.
Structured report contracts and reproducible outputs instead of treating an unvalidated model response as the final deliverable.
AI implementation and improvements
The most valuable work was making the AI behavior more dependable: separating the legal reasoning stages, improving citation QA gates, tightening report contracts, and addressing stale execution identifiers that could make a complex pipeline difficult to reproduce.
This project demonstrates experience integrating AI into a high-consequence workflow where traceability, data isolation, failure handling, and reproducibility matter as much as the model call itself.
Engineering lessons
The platform reinforced a practical principle: AI systems need product architecture around them. Clear state transitions, source-grounded outputs, testable interfaces, and organization-level authorization are what turn a prototype into a professional tool.