---
title: "AI Evaluation & LLMOps Services"
canonical: https://wavect.io/services/ai-evaluation-llmops/
language: en
description: "Evaluate existing LLM, RAG and agent systems. Wavect builds regression datasets, trace analysis, release checks and cost monitoring around real tasks."
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

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.

THE ENGAGEMENT

## Define the task. Observe the run. Gate the change.

1. **Capture expected behavior** Representative tasks, failure cases and review criteria.
2. **Evaluate each change** Prompt, model, retrieval and tool versions tested together.
3. **Operate with context** Traces, cost budgets, alerts and an incident learning loop.

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](/services/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](/services/artificial-intelligence/); trained-model retraining infrastructure belongs to [MLOps](/services/mlops/).

## Questions before you start

### Can you assess an application you did not build?

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.

### Do evaluations prove that hallucinations are impossible?

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.

### Must all production conversations be stored?

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.

### Can you reduce model costs?

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.

## Continue with the right scope

- [Test permissions and AI abuse paths](/services/ai-security/)
- [Build a new AI product](/services/artificial-intelligence/)
- [Operate trained-model lifecycles](/services/mlops/)

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