We test AI agents together with the expert review around them: where an agent fails, whether reviewers can still catch it, and what needs to change in the agent, the process, or the way people work with it. Built for teams where a single error risks financial loss, audit failure, or regulatory fallout.
Find where the system fails, using your own cases.
Recreate those failure conditions so they can be tested on purpose.
Set up the monitoring and repair checks your team will run.
Your team owns the methods, tests and tooling. No managed service, no lock-in.
Our product and engineering teams learned how to think through and design detectors for domain-specific work.
“In our work with Reins AI, our product and engineering teams learned how to think through and design detectors for domain-specific work, to go beyond basic, standard measures. This is allowing our product teams to think about the designs up front as requirements are formed.”
I now have a strong foundation to build on and expand this work across our products.
“The most useful contribution Reins AI provided was their insight and collaboration on AI evaluation and monitoring. Not only did we build an end-to-end evaluation pipeline, but the documentation and connections to the business helped deepen my understanding of AI evaluation and monitoring. Thanks to Reins AI, I now have a strong foundation to build on and expand this work across our products.”
Working with Reins AI was transformative to our existing thinking on medical AI benchmarking.
“Working with Reins AI was transformative to our existing thinking on medical AI benchmarking. The team brought in diverse expertise spanning across strategy, science and regulatory.”
Reliability evidence only helps if others can read and trust it. So we contribute to the standards and open work that shape how AI systems are tested and recorded.
We contribute to OpenTelemetry’s generative AI conventions, working on how evaluation results record who produced them and how. That’s the detail an auditor needs to rely on independent evidence.
See our proposal on GitHubWe’ve worked with MLCommons on the business case for AI safety benchmarks and on medical AI benchmarking.
Our approach is documented in essays and research, not kept behind a sales call.
In client work, we use the tools your team already has: no new licenses, no mandatory dashboards.
Read our research
