AI Evaluation & LLMOps
Know what changed in your AI before customers discover it.
A prompt change can improve a demo and break a workflow nobody retested. We help teams with existing AI applications turn expected behavior into repeatable evaluations, then connect results, traces and operating costs to release decisions.
DIRECT ANSWER
AI Evaluation & LLMOps measures and operates existing LLM applications through versioned task datasets, regression checks, traces, latency and cost visibility. The scope is application behavior across model, prompt, retrieval and tool changes.
Best for
- Teams shipping an AI product without trustworthy regression evidence
- Owners who cannot explain why an agent failed or why its cost increased
Consider another approach
- A new AI product that still needs end-to-end development
- An independent assessment of unauthorized access or abuse paths
Measure the task the customer needs completed
We identify the steps that make a response useful: retrieving the correct source, answering with support, selecting the right tool and stopping when permission or evidence is missing. The dataset includes ordinary cases, previously observed failures and difficult boundaries. Expectations distinguish acceptable variation from a wrong business action.
Choose checks that can justify a release
Deterministic assertions suit schemas and tool arguments; human review suits ambiguous quality judgments. Model-based graders can help, but are calibrated against reviewed examples rather than treated as unquestionable judges. We record dataset versions and test conditions so results remain comparable. Independent permission and abuse testing belongs with AI Security.
Follow a failed request across the system
We connect model calls, retrieval results, tool execution, retries and fallbacks into a trace your team can inspect. Redaction and retention are agreed before collecting production content. Cost and latency are measured per useful task where possible, including failed attempts. The instrumentation should explain a failure without collecting every sensitive input by default.
Illustrative acceptance: a cheaper model misses a required step
In an example comparison, a candidate model costs less per call but skips a required lookup before answering. The regression set exposes the omission, and the release remains blocked for that workflow. The review considers task success, latency and total run cost together. A trace shows whether the problem began in retrieval, routing or the generated response.
Make the next release easier to assess
Deliverables include the evaluation dataset, scoring rules, runner configuration, dashboards and release criteria. We establish a process for adding reviewed production failures without contaminating the held-out tests. Owners decide when a quality change is acceptable and when to roll back. For new application delivery, use AI Agents & Products; trained-model retraining infrastructure belongs to MLOps.
Questions before you start
Yes. We need a callable test path, expected behavior and enough observability to investigate failures. An initial assessment establishes what can be measured before instrumentation changes.
No. They show performance on stated tasks and test conditions. We document coverage limits and define abstention, review or fallback for cases outside reliable evidence.
No. We define the minimum useful trace fields, redact sensitive content and set access and retention limits. Some evaluations can use synthetic or approved redacted examples.
We can compare routing, caching, prompts and model choices against the same task criteria. A cheaper response that fails the workflow is not an accepted saving.
Assess your AI evaluation coverage
Describe the AI workflow, a recent failure or change, and how you currently decide a release is safe enough. Start with a redacted example, not customer conversations.