AI Evals
Meaningful AI evaluation starts with real traces — what your system actually does in response to real users. We build evaluation frameworks on production data that give you reliable evidence of what's working and what isn't.
Software tests verify that code does what you wrote. AI evaluation addresses a harder problem: verifying that a system does what you intended, across the range of inputs your users will actually send.
Most teams deploying AI systems run manual spot checks during development. They test a handful of examples, the outputs look reasonable, and they ship. Six weeks later, a customer finds an edge case that produces a completely wrong answer.
The gap is that AI systems don't fail predictably. The same prompt with slightly different phrasing can produce a different result. An agent that handles most customer queries correctly may consistently fail on a specific class of question. You only know what you measure.
We build the measurement infrastructure that tells you what's actually happening.
How our eval methodology works
We start with traces — the actual records of what your AI system did in response to real inputs. Traces are the source of truth. Code tells you how the system is designed. Traces tell you what it actually does. We analyze traces before writing a single evaluator.
60–80% of eval work is understanding what your system gets wrong and why. We categorize errors by type, find patterns, and prioritize by frequency and severity. This analysis drives everything else: which evaluators to build, what thresholds to set, and whether the problem is in the prompt, the retrieval system, the model, or the task design.
We build pass/fail evaluators rather than 1–5 scales. Scores introduce noise: raters disagree about whether something is a 3 or a 4. Pass/fail is cleaner, more actionable, and easier to trend over time.
Generic evaluators measure generic quality. Custom evaluators measure whether your system is doing the specific thing you built it to do. We build evaluators against your actual use cases, your data, and your definition of correct behavior.
Automated evaluators can be wrong. We build human review into the process at the points where automated evaluation is insufficient: novel error categories, edge cases, and calibration of the automated evaluators themselves.
The eval infrastructure we build
Evaluators that run on every code change, built from patterns found in real production traces. The same way tests catch regressions in software, CI evals catch quality regressions in AI systems. We set up the pipeline, write the evaluators, and establish the thresholds.
Continuous evaluation of production traffic. We instrument your system to sample live interactions and run them through evaluators, giving you ongoing visibility into how the system performs at scale.
Real-time evaluators that run before responses are returned to users. When an evaluator flags a response as problematic, the system can retry, escalate to a human, or return a safe default. This protects against the worst failures.
Structured evaluation on demand: when you change a prompt, upgrade a model, or add a capability, you want to know whether the change helped or hurt. We build the tooling and run the analysis.
What we deliver
A curated set of test cases covering your core use cases, known failure modes, and edge cases. This dataset becomes the benchmark you run against as the system evolves.
Custom evaluators — automated and human review rubrics — calibrated to your use cases. We build these against your data, not generic benchmarks.
Evals wired into your CI/CD pipeline so quality regressions surface before deployment. We set up the pipeline, establish thresholds, and document the workflow.
A clear categorization of what your system currently gets wrong, how often, how severely, and what’s driving each failure category. This is the document that tells you where to invest engineering effort.
Ongoing visibility into production performance. You can see trends, catch new failure categories emerging, and measure the impact of changes.
Manual spot checks work when a system handles a few hundred interactions per week. They don't work when it handles tens of thousands.
At scale, a 2% error rate means thousands of failed interactions per day. A system that looks good in testing can have systematic failures that only surface under real production traffic patterns. A model upgrade that improves average quality can simultaneously introduce a new failure mode on a specific class of input.
Reliable AI systems at scale require measurement infrastructure. Measurement infrastructure requires upfront investment. The alternative is discovering failures from user complaints rather than from your own monitoring.
Evals engagements deliver the most value for teams that:
We also work with teams building AI products that need to demonstrate reliability to customers or auditors.
How we engage
An engagement that delivers a working eval infrastructure: dataset, evaluators, CI integration, and monitoring. Best for teams deploying a new system or establishing measurement for an existing one.
An intensive analysis of an existing system’s failure modes. We analyze your traces, categorize errors, and deliver a prioritized report of what to fix and why. Best for teams that already have a deployed system and want to understand what’s going wrong.
Continuous evaluation support as part of an AI enablement engagement. We maintain the eval infrastructure, update evaluators as the system evolves, and run ad-hoc analysis on significant changes.
Bring us a deployed AI system and your trace data. We'll show you what's happening in your first session.