Consulting / Evals

AI evals for production decisions.

Gyde defines repeatable tests, representative datasets, review protocols, and release gates from prototype through production.

Talk to an AI architect See our approach
The quality system Representative scope

Test → Explain → Gate → Monitor

Useful evaluations link every failure to a task, risk, owner, and release decision.

Task success Safety boundaries Regression Human agreement
01Application-level evaluation 02Human + automated review 03CI and production signals

Why this layer matters

Production releases require representative evaluation coverage.

AI quality is probabilistic, context-dependent, and easy to overfit by inspection. Teams need agreed criteria, representative cases, and regression results for every material change.

01

Subjective acceptance

Stakeholders use different definitions of accurate, useful, safe, and on-brand.

02

Unrepresentative tests

Happy paths dominate while rare, ambiguous, adversarial, and high-impact cases remain invisible.

03

Missing release criteria

Metrics accumulate without an agreed threshold, owner, or action when performance changes.

What we deliver

One engagement, three connected workstreams.

01

Evaluation design

Translate product outcomes and risk into a task taxonomy, rubric, thresholds, and decision protocol.

  • Failure-mode workshop
  • Metric and rubric specification
  • Release policy
02

Dataset & harness

Build representative test cases with provenance, expected behaviour, slices, and repeatable execution.

  • Golden and challenge sets
  • Automated evaluation pipeline
  • Human review workflow
03

Release & monitoring

Connect offline tests to CI, experiments, production sampling, incident review, and dataset refresh.

  • Regression gates
  • Production quality dashboard
  • Evaluation operations runbook

The engagement

Each phase answers a production question.

The initial scope is narrow. Each phase produces working software and a reviewable deliverable for the next decision.

1

Define

Name what good means

Align product, domain, risk, and engineering owners on observable acceptance criteria.

2

Represent

Build the test set

Cover common tasks, edge cases, risks, segments, and known historical failures.

3

Calibrate

Validate the judges

Measure human agreement and test automated evaluators against reviewed examples.

4

Operationalize

Make quality a gate

Connect evaluation results to release, rollback, investigation, and dataset updates.

What you leave with

Deployed software, test results, and an operations runbook.

The engagement includes implementation documentation and a defined handover.

01

Shared quality contract

Rubrics, thresholds, risk tiers, and decision owners that product and engineering can use together.

02

Evaluation harness

Versioned datasets and repeatable tests for prompts, retrieval, models, tools, and complete workflow behaviour.

03

Release report

Results support ship, hold, rollback, or investigate decisions with slice-level visibility into each change.

Typical building blocks

Golden datasetsLLM-as-judgeHuman reviewCI gatesTracingDrift monitoring
Read the enterprise AI evals guide ↗

Questions

Before we begin.

Can an LLM evaluate another LLM?

Yes, for carefully defined criteria and after calibration against human-reviewed cases. Automated judges remain subject to calibration, monitoring, and human review.

How do we create the first test dataset?

Use representative workflow examples, domain expert interviews, available production traces, and deliberately constructed edge cases. Keep the first set small enough for detailed review.

Are offline evals enough?

No. Offline tests support controlled comparison; production monitoring reveals new traffic, context, user behaviour, and integration failures. The two need a feedback loop.

Bring a defined business constraint

Bring us the workflow that is stuck.

We will define a focused engagement using representative data, real permissions, and measurable success criteria.

Talk to an AI architect