Why Ayneye
Video AI products need durable state, not only bigger context windows.
Raw video is expensive, hard to debug, and difficult for agents to use safely. Ayneye materializes reusable artifacts so product teams can build with evidence and cost visibility.
The product thesis
Most video AI demos process a short clip and produce a polished answer. Production systems face repeated questions, dashboard refreshes, support reviews, audit requirements, and budget constraints. Ayneye separates the expensive video-understanding step from repeated downstream queries by turning video into structured state and evidence.
| Problem | Ayneye answer |
|---|---|
| Repeated visual context | Durable World-State and evidence artifacts |
| Unclear answers | Evidence-required Ask Video contract |
| Hidden cost | cost.json and visible sandbox limits |
| Unsafe agents | Read-only tools and blocked high-risk actions |
| Hard scenes | review_required, fallback, and escalation boundaries |
When Ayneye is a good fit
Ayneye is strongest when teams need operational video search, structured event state, evidence citations, repeated questions over the same footage, agent-readable memory, and budget visibility. It is not positioned as a universal replacement for large GPU video models.