Hopit

AI that gets better at its job by doing it.

Hopit is a continual-learning lab. We build models and harnesses that learn new tasks, keep what they already know, and improve inside the enterprises that use them. → hopit.ai

Decision models

Small models that answer a typed decision question in one forward pass, with a calibrated probability for each option.

Both are released for research and demonstration only — see each card for its licence and training data. Code: hopit-ai/hopper.

Continual learning

Our update method lifted an internal tool-use evaluation from 57.9% to 66.1% without losing earlier abilities on the retention suites we track. That is one seed and an internal measurement; we will publish the protocol and artifacts before treating it as established.

Track record

Before continual learning, we built open models and public benchmark suites for fashion, and held ourselves to them in public. They remain the standard our newer work has to clear.

How we publish

• Every rank is quoted with its qualifier.
• Protocols are frozen before inference, predictions are hashed before labels open, and scorers fail closed.
• The runs we lose are published beside the runs we win.

Work with us

We deploy with a small forward-deployed team inside your environment. Your data and your deployed models stay yours. The approach is ideal for regulated enterprises. → hopit.ai

_{¹ JevBench v1.4.2, 24 September 2026 snapshot, scored by an independent maintainer. Second on capability; fifth on the composite score, which also weighs speed and cost. ² Jev Decision Index 0.2.1, 28 September 2026: Hopper (G) 1.2 is third of 46 systems under 5B on the chance-corrected headline score (40.77), within 0.1 of fourth, and 18th of 70 overall. The edition is 76% scored.}