Theo AI / Production system
Inbox to source-attributed case intelligence.
A durable AI pipeline for turning fragmented emails and documents into structured, traceable work product.
- Role
- Founding AI Engineer
- Focus
- Architecture + implementation
- Domain
- Legal AI
- Timeline
- 2025 - Present
AI was not the difficult part. Keeping the evidence connected was.
Enterprise legal work arrives as threads, attachments, duplicates, inconsistent identifiers, and documents whose meaning depends on the matter around them. The system needed to recover that structure without losing the source trail operators use to verify an answer.
A lineage graph made intake traceable.
I designed and implemented the graph schema and persistence layers connecting messages, attachments, normalized documents, threads, and downstream case decisions. That graph became both an audit trail and a retrieval surface for matching new material to existing matters.
Content-addressed documents prevent repeated processing when the same file appears across a thread.
Deterministic identifiers and thread signals reduce the candidate set before model-based verification.
High-confidence paths attach directly; uncertainty falls back to the fuller matching workflow.
Outputs preserve the path back to messages, documents, and evidence used to produce them.
Measured impact
Measured on internal production benchmarks. Client names, corpus details, implementation thresholds, and proprietary prompts are intentionally omitted.