HITL AI Evaluation
Whether you are building software with AI components, implementing your own Agentic RAG Harness, or building foundational and domain-specific models, URI has developed a comprehensive suite of services to ensure your AI does exactly what it’s supposed to do, and nothing it shouldn’t. By combining credentialed domain experts with rigorous human-factors science, we go beyond automated benchmarks to measure factual accuracy, regulatory compliance, and behavioral trust, before launch and throughout the life of your deployment.
Establishes what the AI Implementation or dataset must do and for whom.
Translates the requirements & use cases into a concrete label schema, quality thresholds, and evaluation rubric.
Brings credentialed practitioners into scoping to define authoritative standards and regulatory requirements before annotation begins.
Builds the ground-truth corpus against which all model evaluation is measured, using credentialed domain experts.
Expert-authored prompt-response pairs demonstrating correct domain reasoning for model fine tuning: strictly separated from the Golden Dataset to prevent evaluation contamination.
Domain-expert human evaluation of candidate base models on the client’s specific task set to select the optimal starting point.
Independent stratified audit of existing datasets for labeling errors, coverage gaps, and annotator consistency.
Systematically tests outputs against safety standards and content policies across the full capability range, with human expert judgment on high-risk outputs.
Examines the dataset and model outputs for systematic disparities across demographic groups before they become production incidents.
Regional SME panels assess model outputs for cultural appropriateness, contextual fit, and local regulatory alignment, going beyond basic linguistic accuracy.
Credentialed practitioners review model outputs against authoritative domain standards, producing a defensible accuracy record for regulatory use.
Measures model performance against the client’s golden dataset on their specific tasks, producing a pass/fail verdict against defined thresholds and human-ai reliance.
Identifies the breaking points of both the AI model and the deployed system before real users find them; testing the model’s cognitive limits pre-interface, then the system’s architectural resilience once deployed with real tools and APIs.
Evaluates the psychological and behavioral reliance users place on the AI system, specifically measuring automation bias, system disuse, and escalation rubber-stamping.
Translates evaluation work into formal documentation required for regulatory submission, audit review, or enterprise procurement.
Evaluates whether model outputs are accessible to users with disabilities and whether the AI performs equitably across assistive technology interaction patterns.
Tests the model against actual production inputs to validate that golden dataset benchmark performance transfers to real conditions. Distribution shift between evaluation data and production traffic is the most common cause of post-release degradation.
Provides systematic ongoing measurement of production output quality, detecting degradation early enough to intervene before it becomes a user-facing problem. Model quality degrades through distribution shift, knowledge decay, and edge case accumulation at scale.
We use actual doctors, lawyers, financial analysts, and other credentialed specialists to evaluate complex domain reasoning, not generic crowd workers.
We go beyond “is the model mathematically correct?” to ask, “Will employees blindly trust it?” We measure Automation Bias using validated UX and psychometric frameworks.
We test against hard regulatory frameworks (OWASP, NIST, WCAG) to deliver the exact paperwork your legal and compliance teams need to launch safely.
As the second in our Evaluating Enterprise AI series, this original primary research conducted by URI goes beyond what teams test to expose who is actually doing the testing, revealing a structural blind spot in how enterprise AI gets evaluated. We surveyed 372 professionals working on active AI initiatives, and the data reveals a paradox: evaluation is nearly universal, yet almost no one is actually accountable for it.
In this report you’ll learn:
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In the second installment of our Evaluating Enterprise AI Series: Who’s Actually Testing the AI?, URI turns the lens from what teams test to who is actually doing the testing. We surveyed 372 professionals working on active AI initiatives, and the data reveals a structural blind spot hiding in plain sight: evaluation is happening almost everywhere, but it’s almost no one’s actual job. Teams are running it as a side project instead of bringing in dedicated evaluators to own it.
In this report series you’ll:
Enter your information below to receive the report.
We respect your privacy. By clicking, you agree to receive this content and occasional updates from us. View our Privacy Policy. We’ll also send you occasional UX insights. You can unsubscribe at any time.
REPORT 1
See exactly where automated metrics fall short and find out what it will take to safely deploy AI, avoid costly consequences, and mitigate unpredictable user behavior.
REPORT 2
See exactly where automated metrics fall short and find out what it will take to safely deploy AI, avoid costly consequences, and mitigate unpredictable user behavior.
REPORT 3
See exactly where automated metrics fall short and find out what it will take to safely deploy AI, avoid costly consequences, and mitigate unpredictable user behavior.