AI output assurance
Assay Bench
A workbench for measuring the quality and checking cost of AI outputs when there is no simple answer key.
The question behind the system
The cost that constrains an AI process may not be generation. It may be the cost of becoming confident enough to trust the output.
What it is designed to do
- Report quality with uncertainty and explicit blind spots
- Plan samples against both confidence and checking cost
- Keep judged cases, methods and human effort inspectable
- Study domain
- Synthetic contract exceptions
- Product
- Next.js + SQLite
- Companion
- Separate experimental rig
The public boundary
What this page does—and does not—open.
The application sits beside a process rather than controlling it. This public profile includes no seed database, credentials, model endpoints, client material or live review workflow.
Separate application
Read the monitoring study No public deployment is linked yet. The introduction remains available without exposing a private service or local data.