Real workflows
for real teams.

The problem: Early-stage teams spend weeks gluing together prompts, cron jobs, and Zaps to ship one AI feature.

Early-stage teams spend weeks gluing together prompts, cron jobs, and Zaps to ship one AI feature — and none of it is easy to debug or hand off.

How Hypero solves it

With Hypero, you prototype the feature on the canvas, expose it as an API, and embed it in the product. You iterate on the same thing that runs in production — no separate staging script.

The problem: Ops teams own the long tail of business logic.

Ops teams own the long tail of business logic — invoicing edge cases, onboarding exceptions, vendor approvals — and figuring out how something works means finding the right person.

How Hypero solves it

Hypero gives ops a visual canvas they own end-to-end. Agents handle the judgment calls — classification, drafting — deterministic steps handle the writes and approvals, and every run is logged.

The problem: AI-native startups outgrow notebooks fast.

AI-native startups outgrow notebooks fast — but going from a working agent in Jupyter to a production endpoint usually means rewriting everything in TypeScript.

How Hypero solves it

Hypero lets you run that notebook agent in production without rewriting it. Prototype on the canvas, ship to the same endpoint, and use the trace viewer to debug it when something behaves unexpectedly.