Challenge
Most teams can either build an AI model or run it in the field — rarely both. Labeling is usually the bottleneck before either happens.
FIELD GUIDE · BUILD
Athena OS
Tech startup for no-code AI labeling and the Athena Family model stack — Athena Vision ships today; AEGIS-Q compresses under a quality gate.
In progressAthena
Role
Founder — product direction, Athena Family models, AEGIS-Q, and public technical writing.
Design decisions
- Label one, review the rest: AI drafts; humans decide what becomes training data.
- Ship Athena Vision first (Segment + Detect); keep the rest of the family contract-first until ready.
- AEGIS-Q is allowed to refuse compression that fails its accuracy gate.
Screenshots and media
Current state
Public product surface and technical field notes are live. Treat the product as a working prototype under active development—not a finished commercial claim.
What I learned
- Dialect speech needs reviewers who understand the language; auto-transcription is a starting point, not ground truth.
- Assisted CV works best when one careful label teaches drafts across a set that humans still review.