AI Evaluation Engineering
Test how your AI workflow behaves before the next release. Task-specific datasets, calibrated graders, and regression checks your team can maintain.
What happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
Know What Changed Before You Ship
A model upgrade improves the demo but breaks an existing workflow. A retrieval change produces fluent answers with weaker evidence. Your traces show what happened; you still need tests that distinguish acceptable behavior from a regression.
We build evaluation systems around the outcomes your application must deliver. Start with one workflow, its known failures, and the person accountable for accepting a release.
What We Build
| Deliverable | What Your Team Can Do |
|---|---|
| Versioned evaluation dataset | Replay representative tasks and failure cases with controlled inputs and expected outcomes |
| Task-specific graders | Check outputs, tool effects, retrieval evidence, and escalation behavior; calibrate subjective judgments with domain reviewers |
| Regression harness | Compare model, prompt, retrieval, or tool changes in your existing development and CI workflow |
| Release evidence and handover | Inspect failures, judge disagreement, latency and cost; maintain the suite after delivery |
We use your existing evaluation tooling where it fits. The work is in choosing meaningful tests, checking the graders, and connecting results to release decisions.
Test the Outcome, Not Just the Answer
A support agent can say a request is complete while leaving the underlying record unchanged. A useful test inspects that record as well as the response. It also checks when the agent should defer rather than act.
In Vox, our voice-agent reference platform, internal tests covered silence, addressed responses, opt-out, interruption, context boundaries, and artifact review. That is the level of behavioral specificity an evaluation needs. The case documents those tests, not a client-wide reliability result.
A Scoped Implementation
Bring an existing workflow, authorized test data, and a technical owner. We agree the behaviors to test, access boundaries, acceptance criteria, and maintenance responsibility before implementation. The handover includes runnable checks and an explanation of what they do and do not establish.
Passing the suite is evidence for the covered cases, not a guarantee of every production outcome. Your team retains the release decision and production monitoring.
If the engineering work is already defined, start with a scoped build. If the main uncertainty is what needs fixing, the Production AI Readiness Review diagnoses the gaps. For missing traces, cost attribution, or latency visibility, see AI Observability Engineering.
Related articles
The Difference Between AI Monitoring and AI Observability in Production
Monitoring detects configured signals; observability connects a request's steps so teams can investigate why an AI workflow produced an outcome.
AI AgentsWhen an AI Agent Should Refuse: Designing Rejection Into Production Systems
Design agent refusal around permissions, missing inputs, evidence gaps and approval. Test blocked effects, useful escalation and false refusals.
RAGEnterprise RAG Data Ownership: Freshness, Scope, and Supersession
Make source ownership reach the RAG answer: define authority, propagate supersession, enforce eligibility and test access before expanding the corpus.
Discuss your evaluation workflow
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.