What is AI Evals?
AI evals (short for evaluations) are structured tests that score an AI system's outputs against a defined standard. Instead of eyeballing a handful of sample conversations, teams build a dataset of inputs with expected outcomes and measure performance run over run.
In customer support, an eval set is usually a few hundred to a few thousand real tickets paired with the correct resolution, the correct action, and the correct escalation decision. Each new model version, prompt change, or knowledge base update gets scored against that same set.
Evals cover more than accuracy. A mature suite tests refusal behavior, tone, tool-calling correctness, grounding in approved sources, and whether the agent leaks data it should have redacted.
Why AI Evals Matter
Support AI fails quietly. An agent that starts citing a deprecated refund policy will keep resolving tickets at the same rate while sending wrong answers, and CSAT damage shows up weeks later. Evals catch that gap before customers do.
They also settle procurement arguments. Vendors quote resolution rates measured on their own traffic, which tells you nothing about your ticket mix, so buyers increasingly run their own eval set during a pilot. Teams comparing platforms on accuracy and hallucination benchmarks use held-out tickets the vendor never sees.
For regulated teams, evals are audit evidence. A documented test suite showing how the agent handles PHI, disputes, or medical questions is what infosec reviewers ask for during a vendor security review.
How AI Evals Work
Start with a golden dataset: real conversations, labeled by senior agents, weighted toward the intents that carry the most volume and the most risk. Sampling only easy tickets produces a suite that passes everything and predicts nothing.
Scoring uses three methods, usually together. Exact-match or programmatic checks for structured outputs like refund amounts and API calls, LLM-as-judge for open-text quality against a rubric, and human review on a sampled slice to keep the judge calibrated. Adversarial cases belong here too, which is where evals overlap with adversarial testing of support agents.
The suite then runs in CI on every change and on a rolling sample of live traffic. Continuous scoring is what detects knowledge freshness and drift problems and feeds the dashboards that support leads watch, similar to how teams track containment and resolution quality week over week.
How Fini Approaches AI Evals
Fini runs evals continuously against customer-specific ticket history, which is how we hold 99% accuracy across 3M+ monthly resolutions and 130+ languages. Every deployment ships with a sandbox phase during the 30-day go-live so teams score the agent on their own tickets before a single customer sees it, and PII Shield redacts sensitive fields in real time so eval datasets never become a compliance liability under HIPAA or GDPR.
Pricing follows the same logic. Billing is per resolution rather than per seat, and the Zero Pay Guarantee means if we do not hit 80% resolution in 90 days, you pay $0. To see the eval reporting on your own ticket data, book a demo.
What does AI evals mean?
AI evals are repeatable tests that measure an AI system's output quality against labeled expectations. In support, that means scoring whether an agent gave the right answer, took the right action, cited an approved source, and escalated when it should have. Fini runs these continuously rather than once at launch, so accuracy regressions surface within hours instead of after a CSAT drop.
How do you build an AI eval set for customer support?
Pull 300 to 1,000 resolved tickets that reflect your real intent mix, then have senior agents label the correct resolution and action for each. Weight toward high-volume and high-risk intents, include known edge cases, and hold a slice back so vendors cannot tune against it. Refresh quarterly as products and policies change.
What is the difference between AI evals and AI benchmarks?
Benchmarks are public, standardized tests that compare models against each other on general tasks. Evals are private and task-specific, built from your tickets, your policies, and your tone requirements. A model can top a public leaderboard and still fail your eval set because it does not know your refund rules.
Are LLM-as-judge evals reliable?
They are reliable enough for open-text quality scoring when the rubric is specific and the judge is calibrated against human labels on a sampled slice. Judges drift toward rewarding length and confidence, so pair them with programmatic checks for anything structured, like refund amounts, API parameters, or escalation flags.
How often should you run AI evals?
Run the full suite on every model, prompt, or knowledge base change, and score a rolling sample of live conversations daily. Knowledge bases change far more often than models do, which makes continuous production scoring the higher-value habit. Monthly-only evaluation misses the regressions that matter most.
Do AI evals help with compliance audits?
Yes. A documented eval suite showing how the agent handles sensitive data, refusals, and escalations gives auditors evidence that controls work in practice, not just on paper. Reviewers assessing SOC 2 Type II or HIPAA readiness increasingly ask for test coverage on redaction behavior and out-of-scope question handling.