AI & LLM Evaluation
AI & LLM Evaluation Services - Measure It, Don’t Assume It
AI evaluation systematically measures a system’s accuracy, reliability, safety, latency, and cost against your specific use case, using test sets built from your real scenarios rather than public benchmarks. We run model selection studies, build evaluation suites, perform safety and robustness testing, and independently verify vendor claims.
What We Provide
This has quietly become one of the most consequential capabilities in applied AI, and one of the least practiced. Two facts explain why it matters: Public benchmarks don’t predict your performance. A model topping a leaderboard may be worse than a smaller, cheaper alternative on your particular documents, your terminology, your edge cases. The only benchmark that matters is your workload. AI systems change behavior silently. A prompt tweak, a model version update, a change in retrieved content - any of these can shift quality without a single error appearing in your logs. Without an evaluation suite, you find out from a customer. An AI system without an evaluation suite isn’t finished. It’s unmeasured.
Model Selection Studies
Model selection studies. Head-to-head testing of candidate models - commercial and open-weight - on your actual workload, scored on output quality, latency, and cost per task. The outcome is a decision backed by data, in a market where model choice is too often made on fashion or on whichever vendor got the meeting.
Evaluation Suites for Your Systems
Evaluation suites for your systems. Golden test sets built from real cases, automated scoring (including model-graded evaluation where appropriate), and regression testing wired into your release process. Every prompt change and model upgrade gets verified before users see it.
Safety and Robustness Testing
Safety and robustness testing. Hallucination and groundedness rates, behavior on edge cases and adversarial inputs, refusal reliability, bias probing, and failure-mode mapping. The goal isn’t a perfect system - it’s a system whose failure modes you know and have contained.
Cost and Latency Profiling
Cost and latency profiling. Real cost per task at expected volume, with optimization options quantified. Naive implementations routinely cost several times a well-engineered equivalent for the same output quality, and the gap compounds monthly.
Independent Vendor Verification
Independent vendor verification. Testing AI tools you’re being sold - on your data, against your requirements - before the contract is signed or renewed. Vendors demo their best case; we test your average one. This is one of the highest-ROI engagements we run, because it’s cheap relative to the contract it informs.
Who This Is For
Teams about to select a model or platform and wanting the decision defensible
Organizations running AI in production with no systematic quality measurement
Buyers evaluating vendor AI claims before a significant commitment
Governance and risk functions needing evidence that AI systems perform as claimed - see AI Governance
Building a Test Set That Means Something
Evaluation quality is capped by test set quality, so this is where the effort goes.
A useful test set is representative - matching the real distribution of cases rather than the interesting ones; includes known failures, since past errors are the most valuable test cases you own; covers edge cases deliberately, including rare-but-costly scenarios; is expert-labeled, with correct or acceptable outputs defined by people who actually know; is versioned, so results stay comparable across changes; and is held out from any tuning, or you’re measuring memorization.
Size depends on task variance. A few hundred well-chosen cases usually beats several thousand assembled carelessly. The test set is an asset that outlives any particular model - it’s the thing that lets you evaluate the model you’ll switch to next year.
Can you prove your AI system is performing?
We’ll build a test set from your real cases and give you a measured baseline you can hold your system to.
What We Measure, Beyond Accuracy
Accuracy alone hides the failures that matter:
Groundedness - is the answer supported by the retrieved source, or fluent invention?
Citation correctness - does the cited source actually say what’s claimed? Frequently not, and rarely tested.
Refusal behavior - does the system decline appropriately when it should, and only when it should? Over-refusal is a real product failure, not a safe default.
Consistency - does the same question asked twice produce materially different answers?
Latency distribution - the tail matters more than the median for user experience
Cost per task at realistic volume, including retries
Degradation profile - how gracefully quality declines on harder inputs, which predicts real-world behavior better than average scores
Safety - behavior under adversarial and manipulative inputs
Continuous Evaluation, Not an Annual Exam
The most consequential shift a team can make is treating evaluation as part of the release process rather than a milestone.
What that looks like: automated regression runs on every change to prompts, retrieval, or model version, with results visible in the pull request; quality gates blocking deployment on regression beyond a threshold; production sampling with periodic human review of real outputs; drift monitoring on inputs, because usage patterns change even when your system doesn’t; and a scheduled deeper review - quarterly is common - reassessing model choice as the market moves.
Without this, a prompt change made on a Friday can degrade quality silently until a customer notices. Silent degradation is the default state of an unmeasured AI system, and the reason evaluation deserves budget rather than good intentions.
Frequently Asked Questions
Groundedness, citation correctness, appropriate refusal behavior including over-refusal, answer consistency, latency at the tail rather than the median, cost per task at real volume, degradation on harder inputs, and safety under adversarial prompts. Accuracy alone conceals most of the failures that matter.
It depends on task variance, but a few hundred well-chosen, expert-labeled cases usually outperforms thousands assembled carelessly. Include known past failures and deliberate edge cases - and hold the set out from any tuning, or you’re measuring memorization.
LLM evaluation measures a language-model system’s accuracy, groundedness, safety, latency, and cost on your specific tasks, using test sets built from your real scenarios. It’s how you know whether a system is good enough to deploy and whether it stays good enough after changes.
Public benchmarks measure general capability on standardized tasks. They don’t reflect your documents, terminology, edge cases, or quality bar - and models that lead on benchmarks routinely lose to cheaper alternatives on specific real-world workloads.
From your real cases: a representative sample covering common scenarios, known edge cases, and previous failures, with correct or acceptable outputs defined by your subject-matter experts. Test set quality determines evaluation quality, so this is where we spend the effort.
Continuously in the form of automated regression tests on every change, plus a fuller periodic review - quarterly is common - as models, content, and usage patterns shift.
Yes. Independent evaluation of third-party systems - accuracy, safety, and cost - is a common engagement, especially before contract renewals or scope expansions.
You’ll get the specific failure modes and the options: prompt and retrieval improvements, model change, scope reduction, or additional human review. Most underperforming systems are fixable once the failures are actually characterized rather than anecdotal.