AI Evaluation
Know whether your AI actually got better before you ship the change. We test your AI on your own real cases, so before every release you know if it got better, stayed the same, or got worse.
Watching your AI after launch is AI Operations. Need the agent itself built first? See AI Agent Development.
Test It Before You Ship It
A score with nothing to compare against isn't worth much. We build the test sets, grading, and release checks that show whether a change to a model, prompt, or agent made things worse, before it ships and not after.
What is AI evaluation?
AI evaluation is testing whether your AI does its job correctly, safely, and consistently. We build a test set from your own real cases, score the AI against it, and rerun it before every release. So when you change a model, prompt, or agent, you know whether it got better or worse before customers do. It's also called LLM evaluation or AI testing.
Tools We Use
Monitoring that catches problems before your users do: metrics, logs, traces, and alerts connected to the testing process instead of added after launch.
Recognize the symptoms
When You Need AI Evaluation
If any of these sound familiar, your AI probably needs proper testing.
- It worked in testing but behaves differently with real users
- A model update made things worse and users noticed before you did
- You have no real test set, just a few manual spot-checks
- Changes go straight to production with no safety check
A score you can't explain isn't proof your AI works.
- AI evaluation shows that your AI system (the app, the agent, the prompts, and how it looks things up) keeps working well enough for its job, across repeated runs, realistic inputs, and every change you make.
- It's different from monitoring. AI Operations watches your AI in production. Evaluation defines what "good enough" means, builds the tests that measure it, and blocks any change that makes things worse.
- We define what the job actually needs, build the test set, and check the grading is accurate before we recommend giving the AI more independence.
- Public benchmarks are a fair reference point, but they don't prove your support agent or lookup assistant works. That proof comes from tests built from your own workflow, not a leaderboard score.
- Gartner's first Market Guide for this category, from February 2026, draws the same line. It treats AI evaluation as inseparable from monitoring (logs, metrics, traces, quality, safety, speed, and cost), because an AI that's fast and cheap but wrong isn't reliable, and one that's accurate in a test but slow or expensive in production isn't either.
What We Build
Eight parts that work together, organized around what shows a change didn't make things worse.
How we build test sets and grading has its own page: Test sets and grading in detail.
AI evaluation
A test set, a scoring method, and a pass mark that turn "seems good enough" into a clear decision. Built around your workflow, not a public benchmark.
Test sets from your data
Test cases from your past tickets, chats, and known failures, labeled by severity. A person reviews at least 20 before we trust an AI grader with the rest.
Regression testing
Tests that run before any change ships. You see the pass rate by severity, what newly broke, and what changes from run to run.
Behavior testing
We check each part: does it find the right information, use tools correctly, format output right, and know when to refuse or hand off to a person?
Tricky-case testing
Deliberately hard and abusive test cases, so we find how it breaks in review, not when a customer tries something unexpected.
Release checks
An automatic ship, hold, roll back, or exception decision based on how severe any failure is. A serious problem can't go out without a named person signing off.
Release reports
At each release: pass rate by feature and severity, cost, speed, and where the AI grader disagreed with people.
Ongoing monitoring
We watch quality in production, and every confirmed failure becomes a new test within a set time.
Building the agent itself (permissions, memory, how it's coordinated) is AI Agent Development. Monitoring dashboards and incident response are part of AI Operations. Testing against prompt injection, jailbreaks, and other attacks is part of AI Security.
What Good Testing Needs
Eight parts that work together, not a handful of manual spot-checks.
A clear definition of the job
Write down what users are trying to do, what a correct answer looks like, what the AI must never do, and what score counts as a pass.
A test set from your real cases
50 to 100 everyday cases plus 10 to 20 tricky or broken ones. We keep adding to it over time.
A way to score answers
Automated checks, plus an AI grader that we compare against scores from real people so we know it's fair.
Ranking by how bad a failure is
Every test case is labeled by how much a failure would hurt. Exceptions need a named person to approve them.
A check before every release
Each change to a prompt, model, or tool runs the tests automatically. The result is one of four: ship it, hold it, roll it back, or approve an exception.
Repeat runs
The same test runs several times, so a pass means it works reliably, not that it got lucky once.
A loop from real failures to new tests
When something goes wrong in production, it becomes a new test case, so the same failure can't slip through twice.
Clear release reports
A report showing what was tested, how it was scored, and where it failed, not just a percentage.
What We Test
Organized around the real system being tested, not the grading technique.
Support and service agents
Tests built from your real tickets and chats. We check whether the agent solved the customer's problem, not just whether it replied.
Document search and answers (RAG)
We score the search step and the answer step separately, so you know whether to fix the search or the response.
Multi-step AI agents
We check every step: which tools it used, what it decided along the way, and what it handed off, not only the final answer.
Writing and content generation
Scoring for tone, accuracy against your sources, and format, checked against real examples of good work.
Scoring and prediction systems
How often it's right, how often it misses, and how sure it is, broken down by customer group instead of one average number.
How a Release Decision Gets Made
Not a single pass or fail flag.
An AI grader, checked by people
One AI model grades another's answers. We compare it against at least 20 answers scored by people, to catch its biases instead of trusting it blindly.
A fixed test set before every release
A locked, versioned set of tests that every change must pass, so a regression is caught in review, not by customers.
Quiet testing on live traffic
A new version runs on real requests alongside the current one. Users never see its answers, but we see how it scores.
Failures weighted by how bad they are
One pass rate hides which failures matter. Scoring by severity gives a release decision that reflects real risk.
What You Receive
AI Evaluation vs AI Operations
AI Operations watches your AI after launch: outages, cost, speed, capacity. AI Evaluation comes first. It defines what "good enough" means, builds the tests, measures whether a change made things worse, and checks that the grading itself is accurate. Operations tells you something broke. Evaluation tells you whether it's a real break or normal ups and downs.
AI Evaluation vs LLM Testing and AI QA
Mostly the same work under different names. AI evaluation, LLM testing, AI QA, and AI observability are close to the same field. Gartner's first Market Guide for this space, from February 2026, treats it as one category. The deliverables are the same whichever term got you here: test sets, graders, release checks, and production monitoring.
From our experts.
Writing on evaluation, regression testing, and agent reliability.
Our AI Evaluation Process
The same nine-phase process we use everywhere, with a checkpoint at every step. For evaluation work, the testing phase means reviewing the test set, checking the grader is accurate, and confirming the release check works, case by case.
Related Engineering Services
Common questions.
Bring us the score you can't explain yet.
Whether it's one agent whose quality score you can't explain, or a whole AI system shipping changes with no safety check, we'll look at where the evidence stands before recommending anything.
No hype · No forced roadmap · Just a clear view of what the evidence needs next