A model that’s 95% accurate sounds impressive until you consider what the other 5% costs in a healthcare setting — a missed denial reason, an incorrect eligibility read, a mishandled patient record. We help you find and close that gap before it reaches production.
This matters whether the model in question is something you built in-house, something you’re evaluating from a vendor, or something Promantra.ai delivered as part of another engagement. AI vendors have a natural incentive to describe their own models favorably — an independent evaluation, run by people who understand both AI performance and healthcare operations, gives you a second opinion you can actually trust before you commit budget or patient data to a new system.
Domain experts review model outputs against real healthcare data, flag failure patterns, and feed corrections back into your model or workflow — creating a continuous accuracy loop rather than a one-time check.
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Expert Review
Domain experts review model outputs against real healthcare data — never synthetic test data.
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Flag & Correct
Reviewers score outputs against a rubric built for your use case and flag failure patterns.
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Feed Back Into Model
Corrections feed back into your model or workflow, creating a continuous accuracy loop.
We typically start with a representative sample of your actual claims, documents, or records — never synthetic test data — because a model’s performance on clean, artificial examples tells you very little about how it will behave on the messy, inconsistent data your organization deals with every day. Reviewers score outputs against a rubric built for your specific use case, and every disagreement between the model and the reviewer becomes a data point for improving the model going forward.
This process is designed to be transparent rather than a black box in its own right. You’ll see exactly which claims or documents were reviewed, what the reviewer’s rationale was for each judgment call, and how that feedback translated into a measurable change in model performance — so the validation itself is as auditable as the model it’s evaluating.
We benchmark model performance against defined accuracy, safety, and compliance criteria specific to healthcare use cases, giving you clear, defensible evidence of how a model performs before it touches production data.
The output of this process is a written evaluation report — accuracy by document or claim type, common failure patterns, and a clear recommendation on where the model is ready for production and where it still needs human oversight. This is the kind of documentation compliance and risk teams typically want to see before approving an AI system for use on real patient or claims data, and it’s often missing from vendors’ own internal testing.
Independent validation for everyone who builds, buys, or relies on healthcare AI
Health-tech companies building AI products, payers and RCM vendors evaluating third-party models, and healthcare organizations that need independent validation before trusting AI with patient or claims data.
We work with each of these groups a little differently.
Health-Tech Companies
Building AI products — typically want ongoing evaluation as their models evolve.
Payers & RCM Vendors
Evaluating third-party models — usually want a one-time, rigorous evaluation before a purchasing decision.
Healthcare Organizations
Often want both — an initial evaluation before go-live, and periodic re-checks afterward to make sure performance hasn’t drifted.
In every case, the goal of the engagement is the same: give you evidence you can actually act on, whether that means approving a model for production use, sending it back for more training, or deciding a particular use case simply isn’t ready for AI yet.
A member of our team will reach out within one business day to schedule a short discovery call — no obligation.