AI Assurance's Performance Monitor continuously measures how your deployed models perform in production. It produces the auditable record your operations, your regulators, and your professional liability require.
Most organizations deploy AI and move on. No baseline. No ongoing measurement. No way to know whether the model performing well at launch is still performing well six months later.
In high-stakes industries, that gap is a liability. Models drift. Input conditions change. Confidence erodes — silently, between audits, while decisions are still being made.
Establish the baseline: Every model starts with a documented performance standard; what good looks like, measured against your own data. Every future evaluation has a verified reference point.
Track what changes: Continuous evaluations run on a defined schedule. Deviations from baseline are flagged before they become operational failures. You know when something is changing, not after it has.
Produce the record: Every evaluation generates a scored, timestamped snapshot. Your leadership gets a current picture of model health. Your auditors get a documented trail they can stand behind.
Single metrics lie by omission. A model can show acceptable accuracy while its input data is drifting, its confidence is becoming erratic, and its response times are degrading. Each metric looks passable in isolation. Together they tell a different story.
The AI Assurance Performance Monitor combines measurements across accuracy, data integrity, operational health, and audit transparency into three composite scores — Model Reliability, Audit & Governance, and Trust & Quality. Each score is a weighted picture of a specific dimension of model health, calibrated to your industry and operating environment.
The result is not a point measurement. It is a running narrative — one that shows whether your AI is stable, whether it is drifting, whether it is operating within your defined standards, and whether those standards are being met consistently over time. That narrative is what holds up in a boardroom, a regulatory review, or a procurement evaluation.
The specific metrics, weights, and scoring methodology behind each composite are proprietary to AI Assurance. What your team sees is the output: clear scores, plain-language status, and a trend line that tells you which direction your model is heading.
On-premises and private networks: Deploys entirely inside your infrastructure. No cloud dependency. Data never leaves your network. Operational on a single server with one command. Built for SCADA, OT, and air-gapped environments.
Cloud: The same platform, deployed as a managed cloud service. No architectural differences between on-prem and cloud, only the infrastructure changes.
Infrastructure & Engineering: When your model influences a structural recommendation, a maintenance schedule, or a safety-critical decision — you need your own performance record, not your vendor's.
Healthcare FDA, ONC, and state-level: AI regulations are moving fast. AIA Performance Monitor gives you the documented performance evidence your compliance program requires before the audit arrives.
Industrial: Sensor drift, environmental variation, and aging equipment create conditions where last year's model makes decisions based on this year's reality. Monitor the gap before it becomes an incident.
The AI monitoring market is built for engineering teams; tracing calls, logging tokens, measuring API response times. Those tools tell your developers what the system is doing.
AI Assurance's Performance Monitor tells your leadership whether the AI is still doing what you need it to do and it gives you the documented proof to stand behind that answer in front of a board, a regulator, or a plaintiff's attorney. That distinction is the product.
The AI Assurance Performance Monitor is in active deployment with early adopter clients.
Early adopters receive direct access to our technical and advisory team, priority onboarding, and preferred pricing on the annual subscription; along with meaningful input into the platform roadmap.