ENTERPRISE AI / 01
Document intelligence, with a human in the loop.
Turning complex documents into structured, reviewable information through AI extraction and human verification.
From information.
To understanding.
The challenge.
AI extraction needs more than a model response. Reviewers need source attribution, clear conflict handling, and a way to protect manually verified information.
My contribution.
Contributed backend APIs and reviewer interfaces for verification, benchmark management, permission-aware decisions, and document-level publishing.
The approach.
A document-owned review workflow combines extracted attributes, evidence references, manual corrections, and publishing previews. Each document retains its own verified information and conflict decisions.
The decisions behind it.
Keep verification close to its source
Document-owned truth tables and source references help reviewers trace an attribute back to the information it came from.
Treat publishing as a deliberate operation
Conflict-aware workflows distinguish added, modified, removed, and unchanged attributes while protecting verified values.
Make permissions follow the workflow
Phase-aware access controls separate benchmark management from live review permissions. Tests cover APIs, publishing, and phase transitions.
What was delivered.
Delivered review and publishing functionality, structured Excel benchmark exports, and backend test coverage. This profile describes engineering contributions; no numerical performance claims are made.
HAVE SOMETHING IN MIND?
Let’s make
something useful.
A thoughtful conversation is where good software begins.