Your underwriting, fraud, and quality-control models make thousands of accept-or-reject calls a day. GAMATIQ scores the trustworthiness of each one, so your team spends its review hours on the handful of decisions that actually carry risk — and lets the rest flow through automatically, saving you time and money.
A model gives an applicant an 86% approval score. But is that 86% built on solid, verified data — or on a self-reported income field, a flagged document, or a test with a known error rate? Today, both look identical on the screen. Your team can't tell which decisions deserve a second look, so they either review everything or trust everything. Both are expensive.
Fraud and underwriting queues are full of clean cases that never needed a human, while the genuinely fragile decisions slip through unexamined.
Decisions built on unreliable inputs turn into mispriced policies, wrong credit lines, and shipped defects, with losses that only surface months later.
Auditors and regulators increasingly want a per-decision answer to why a model was trusted. Aggregate accuracy no longer satisfies the question.
GAMATIQ sits between your model and your decision. It measures how much of each prediction's uncertainty comes from errors in the input data — versus the model itself — and routes accordingly. Nothing about your model changes.
Pass your model's prediction, the inputs behind it, and the known error rates of those inputs — through a single API call.
GAMATIQ isolates the uncertainty caused by unreliable inputs from the model's own uncertainty — and pinpoints which field is responsible.
Reliable decisions flow through automatically. Fragile ones are flagged for review, each with a plain reason your team and auditors can act on.
GAMATIQ applies wherever a model’s inputs carry known, measurable error rates; a lab result, a self-reported field, a verification check. Our product is customized to your decision models and inputs. Select where you would deploy it for your greatest pay-off.
Every flag is produced by the same peer-reviewed decomposition: uncertainty attributable to input error, separated from the model’s own uncertainty, for each individual decision. See the evidence →
GAMATIQ is built on a published mathematical framework and is backed by mathematical proof. It is tested on real and simulated data.
The method rests on formal mathematical results: a variation-ratio measure of prediction uncertainty, derived for binary classifiers with two distinct sources of uncertainty, the model itself and errors in its inputs. Every reliability score traces back to a proof.
Applied to real patient data in a clinical outcome-prediction study, where the error rate of every input is known. The framework pinpointed the exact cases where an unreliable input could change the decision.
Evaluated across ten simulated datasets, including portfolios where 90% of cases sit in one class, and compared head to head with Monte Carlo dropout, the industry-standard approach. The method showed superior uncertainty awareness throughout.
We run a scoped pilot on your real decisions, whether underwriting, claims, fraud, or quality control, usually within a few weeks and with no changes to your existing models. You'll see exactly which decisions GAMATIQ would have flagged, and what that's worth.