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AI evaluation means running systematic tests that tell you exactly which prompts fail, which retrieval paths return the wrong context, and which agent steps produce inconsistent outputs. Not a gut check. Not “it looked fine in the demo.” Scored results against a ground-truth dataset, tracked over time, with a clear signal when something regresses.
Most teams skip the measurement layer, ship, then spend months firefighting regressions they can't diagnose because they have no baseline to compare against. We build the measurement layer first (eval datasets, scoring pipelines, and CI integration) so that every change to your AI system is visible before it reaches users.
Tell us what you're building.
An eval engagement typically covers a subset of these six capabilities, chosen based on what your system does and where the risk is highest.
We build curated golden-set Q&A pairs from your domain. These become the ground truth that every future model change or prompt update is measured against.
Recall@k, MRR, and NDCG measurements for RAG systems. We tell you exactly what percentage of relevant documents your retrieval pipeline is returning, and at what rank.
Using Ragas or a custom scoring pipeline, we measure whether the model's answers are grounded in the retrieved context and whether they directly address the question.
A test suite that runs on every prompt change and catches prompt drift when you update the underlying model. The model changed, your prompt didn't, and now outputs differ silently.
Task completion rate, step count, and tool call accuracy for multi-step agent workflows. We measure whether the agent reaches the goal, not just whether it runs without error.
An automated pipeline that uses a judge model to score open-ended outputs at scale. Calibrated against human annotations so the scores mean something.
These are the most common reasons AI systems degrade silently after launch. Each one is invisible without the right measurement.
The retrieval pipeline returns too many chunks, diluting the relevant context with noise. Typical symptom: answers are vague, incomplete, or draw on the wrong part of the document. We measure this with recall@k and chunk relevance scoring, and fix it by tuning the retrieval threshold and re-ranking strategy.
A model update changed how your prompt is interpreted. You didn't change anything, the model did. GPT-5 mini behaves differently from GPT-5 on the same prompt. Without a regression suite running against a golden test set, you find out about this from user complaints, not from a CI alert.
The agent keeps calling the same tool because the output format doesn't match what the next step expects. Without trajectory evals measuring step count and tool call sequences, this loops until it hits your token limit. We catch it in staging, not production.
A bad eval is worse than none, because it manufactures confidence. A number that does not predict real-world behavior gets quoted in meetings and shipped on. Four properties separate an eval you can stake a release on from a vanity dashboard.
The test set mirrors real traffic. An eval built from the inputs you wish users sent tells you nothing about the inputs they actually send. We sample from real logs, weight the set to the production distribution, and deliberately include the messy edge cases and known failures, so the score moves when the product would.
The judge is calibrated, not trusted blindly. LLM-as-judge scales scoring, but a judge model has its own biases. We calibrate it against human-graded examples until its agreement with your experts is demonstrated, ship the scores with that provenance attached, and re-verify on a sample monthly. A judge score with no calibration behind it is a guess wearing a decimal point.
For agents, the path matters as much as the answer. An agent can reach the right result by calling the wrong tool with someone else's data, or burn ten steps where two would do. Output-only scoring misses all of it. We evaluate the trajectory: tool-call correctness, permission boundaries, efficiency, and whether the agent stops cleanly instead of looping.
The gate lives in CI, or it rots. An eval you have to remember to run is an eval you stop running. We wire the suite into your pipeline so it executes on every prompt and model change, reports per-slice deltas on the pull request, and blocks a merge that regresses below the threshold you set. The point is to catch the regression before a user does, automatically.
Eval work pays back most for teams with real usage. It isn't the right investment in every situation.
Teams that just want a quick demo
Demos don't need evals. Evals are for systems that need to work reliably across the full distribution of real inputs, not just the happy path you prepared for the presentation.
Projects with fewer than 100 real user interactions
We need real usage data to build a meaningful eval dataset. Fewer than 100 interactions doesn't give us enough signal to identify failure patterns. Build the eval infrastructure after you have real users, not before.
Anyone looking for a “100% accuracy” guarantee
Evals tell you where you are and what to fix. They don't make the system perfect. If your use case requires zero tolerance for error, that constraint belongs in the architecture decision, not in the eval target.