Hyperbench

Engineering teams building products that combine hardware and software must track down issues that span both domains and across multiple repositories. Today this requires manually tracing failures, reproducing them on physical hardware, and coordinating fixes across repos before verifying the change actually works on real equipment. This process of understanding, reproducing, repairing, and verifying is described as a manual handoff chain that the company's product aims to replace with AI agents.

Hyperbench offers AI agents that understand a system across both code and hardware, trace issues to their source, and work across multiple repositories to delegate fixes. The product captures failures on the user's own hardware bench, retests fixes on that hardware, and produces draft pull requests for review. It runs locally so infrastructure and control stay with the customer, while sharing context with the team, and it is built to plug into a company's existing custom stack and hardware setup. The workflow is organized around four steps described on the page: understand, reproduce, repair, and verify, with evidence such as code, test results, and recordings reviewed together at each handoff.