Services
Your observability platform tells you the agent ran.
It doesn't tell you whether it got it right.
It doesn't tell you whether it got it right.
In regulated industries like audit, finance, healthcare, and legal, the question that matters isn't execution health. It's domain correctness: did the system apply the right rules, process the right documents, produce outputs that are actually correct? That's a different layer than infrastructure monitoring provides, and it's the layer most teams are missing.
Reins AI builds and transfers a domain evaluation and reliability layer that sits above your existing stack. The framework follows a closed loop drawn from the same reliability engineering traditions that govern aviation, medical devices, and defense, but applied to agentic AI.
Rule-based, statistical, and LLM-based evaluators calibrated to your domain procedures. Quality, suitability, and efficiency signals triangulated into a picture of whether the system is doing the right thing, not just whether it ran.
FMEA-based severity classification routes findings by consequence, probability, and exposure. Deterministic routing to your existing work management tools with evidence attached. Your team works on what matters first, not everything at once.
In regulated domains, reviewers often can't access production data directly. Simthetic© reconstructs failure conditions using synthetic data, creating reproducible test cases without exposing sensitive records. Every reproduced failure becomes a regression test.
Each prioritized failure becomes a repair packet: root-cause hypothesis, proposed fix, acceptance criteria, and the synthetic data to verify it. These accumulate into a knowledge base of what failed, why, and how it was fixed. This is the audit trail.
Failure frequency, mean time to repair, and fix effectiveness rate tracked over time. Not a dashboard. A reliability growth curve that shows your system is getting better and can prove it.
Think of us as a teaching hospital for AI systems. You bring us the system. We diagnose it, simulate the failure conditions, build the instruments to monitor and repair it, and then we teach your team to run it all without us.
Methodology transfers. Not managed services. Your team owns and operates the evaluation layer, the detection method library, the triage logic, and the knowledge to extend it. We build the loop that closes, and then we leave.
Most engagements start with a focused diagnostic: what does quality mean for your specific system, where are the gaps, and what would a reliability layer need to look like? From there, engagements expand into infrastructure build and transfer. The diagnostic is valuable on its own, but it's also how both sides decide whether a deeper engagement makes sense.
