AI Evaluation Services
Crescent AI builds the eval datasets, grading methodology, and pass bars that prove a change to a model, prompt, or agent didn't make things worse — before it ships, built from your own workflow, not a generic public benchmark.
Watching a system once it's live, incidents, cost, uptime, sits with AI Operations & Optimization. This is the pre-release proof, not the production dashboard.
Proven Before It Ships, Not Assumed Safe
A quality score nobody's calibrated is a guess with a decimal point. We build the test set, the grading method, and the pass bar that turn "good enough" from a feeling into a number you can defend.
What is AI evaluation?
AI evaluation is the practice of building a test set, a scoring method, and a pass bar for an AI system, so a change to a model, prompt, or agent can be measured against real cases before it ships, instead of judged by how it feels in a demo.
Why This Isn't Just AI Reliability Engineering
AI Evaluation is the pre-release slice: building the eval dataset, the grading method, and the pass bar that prove a specific change is safe before it goes live. AI Reliability Engineering is the umbrella covering that plus production monitoring, regression gates wired into your release process, and the ongoing loop that turns real failures into new test cases. Evaluation is the foundation everything else in that discipline is built on — it's usually where an engagement starts.
Recognize the symptoms
When You Need AI Evaluation
If two or more of these are already true, this isn't a tuning problem.
- There's no real test set, just a handful of manual spot-checks
- You can't say whether a prompt change made the system better or worse, only that it "feels different"
- An AI-graded quality score exists but nobody's checked it against human judgment
- Public benchmark numbers get quoted but nobody's tested against your own workflow
What We Build
Five parts, engineered as one evaluation system, not a spreadsheet of spot-checks.
Eval Dataset Construction
A test set built from your own tickets, transcripts, and known failures, 50 to 100 typical cases plus 10 to 20 tricky or broken ones, not a generic public benchmark.
Scoring & Grading Methodology
A way to score each answer that fits the job: automated checks, retrieval-quality checks, and an AI grader where a rule-based check isn't enough.
LLM-as-Judge Calibration
Every AI grader checked against at least 20 cases a person has scored, tested directly for known biases like favoring longer answers, before we trust it to score the rest.
Severity Labeling & Pass Bars
Every test case labeled by how bad a failure would be, with a documented process for approving exceptions instead of an undocumented judgment call.
Regression Gate Integration
The eval suite wired into your release process, so a change ends in one of four outcomes: ship it, hold it, roll it back, or approve an exception by name.
Evaluation We've Built
Organized around the real-world system being tested, not the grading technique underneath it.
Support & Service Agent Evaluation
Test sets built from real tickets and transcripts, scoring whether the agent resolved the request, not just whether it replied.
RAG & Retrieval Evaluation
Retrieval quality scored separately from answer quality, so a wrong answer traces back to bad search or a bad response, not a single blended number.
Multi-Step Agent Trajectory Evaluation
Checks on each step in a workflow, tool calls, intermediate decisions, and hand-offs, not just whether the final output looks right.
Generative Content Evaluation
Scoring for tone, factual grounding, and format compliance on generated text, images, or other content, calibrated against real examples.
Classification & Prediction Evaluation
Precision, recall, and calibration checks tracked by segment, not just an aggregate accuracy number that hides where it's actually wrong.
For how we build a test set from scratch against a real agentic workload, see our guide on Benchmarking Production AI Agents.
What You Receive
Related Engineering Services
Common questions.
Bring us the score you can't actually defend yet.
Whether it's a single agent whose quality score you can't explain, or a whole system shipping changes with no test set behind them, we'll walk through where the proof stands before we recommend anything.
No hype · No forced roadmap · Just a clear view of what the evidence needs next