A measured baseline
A documented view of current quality, failure modes, latency, and cost within the access and sample available.
AI evaluation & optimization
AI evaluation and optimization. Measure the whole task.
Find where an existing AI workflow loses quality, time, or money. Compare changes on the same representative tasks, record the trade-offs, and use the evidence to choose what is worth rolling out.
Evaluation plan
A useful handoff
Same work. A fair comparison.
| Test case | What to inspect | Decision rule |
|---|---|---|
| Policy answer | Answer and cited passage agree | Reject unsupported claims |
| Missing guidance | Uncertainty and escalation | Reject a fabricated answer |
| Routine request | Quality, time, and total cost | Compare with the baseline |
Agree the acceptance criteria before comparing candidates.
Built around your work
When this makes senseRecognize
Recognize
the friction?
A lower token price does not necessarily mean a cheaper completed job. Retries, long prompts, missed context, and human corrections can dominate the result. Optimizing one headline metric can quietly make the experience worse.
A good fit when…
An existing AI feature needs too many corrections, retries, or manual checks.
Requests feel slow or expensive, but you do not yet know which part of the workflow causes it.
You are considering a model, prompt, or retrieval change and need a fair comparison before rollout.
Tangible work
What you take away.
Controlled comparisons
Candidate changes tested on the same representative work, with regressions and uncertain results visible.
A prioritized recommendation
Which changes are worth trying, what they risk, and how to monitor and reverse them if live behavior differs.
Put it to workReal tasks.
Real tasks.
A more useful way through.
Find a starting point for your team.
01
Challenge support suggestions
Test routing and draft replies against mixed-topic tickets, missing policies, and sensitive requests.
Explore the workflow 02Evaluate document findings
Compare extraction and review behavior on missing pages, poor scans, and ambiguous requirements.
Explore the workflow 03Check an inbox assistant
Test whether requests, canceled commitments, and exclusions survive changes to the assistant.
Explore the workflowOne possible workflow
From input to a clear next step- 01 / The starting point
An internal assistant repeats long context in every request.
- 02 / The right context
Establish a baseline using representative tasks, difficult cases, and current usage.
- 03 / Prepared for review
Compare context changes, caching, routing, or models against the same test set.
- 04 / A person decides
Keep a candidate only if it meets the agreed quality and reliability thresholds.
From first conversation to handoffA clear start.
A clear start.
An agreed finish.
- 01
Agree what good means
Choose realistic tasks and acceptance criteria with the people using the outputs.
- 02
Measure before changing
Profile the current path and compare a limited set of changes without moving the goalposts.
- 03
Recommend a controlled rollout
Document trade-offs, rollback criteria, and what should be measured after any approved change.
Agree the scopeAgree the scope
Agree the scope
and the limits.
No guaranteed saving, benchmark score, or promise that a smaller model will preserve quality. Production changes require separate authorization; an evaluation can end with a recommendation to keep the existing approach.
Security and data questions ↗A few useful answers
Before we begin.
What does an AI evaluation engagement deliver?
A scoped evaluation can produce a baseline, a representative test set, controlled comparisons, and a prioritized recommendation. Record failures as well as aggregate results. The recommendation should identify the evidence, sampling limits, expected trade-offs, and checks needed before any production change.
What makes a useful first optimization scope?
Choose one workflow and one decision, such as whether a model or retrieval change improves it. Define acceptable outputs with the people using them. Include ordinary tasks and known failures, then run the baseline and candidate on the same cases using consistent scoring.
What determines the cost of an AI evaluation?
Cost depends on the number of workflows and candidates, test-set preparation, access to usage information, and the amount of human review. Model calls and repeated runs can add evaluation expense. Agree the decision the evaluation must support before expanding the experiment.
How long does an optimization review take?
Timing depends on whether a useful test set exists, how easily the current workflow can be reproduced, and who can judge output quality. Missing traces or disputed success criteria add preparation work. The plan should separate baseline preparation, comparison runs, and review of the results.
Can you evaluate AI without production access?
Sanitized examples, exported usage records, and a reproducible test environment may support an initial review. State what that evidence excludes, such as unusual live traffic or permission behavior. Production access and any production changes should be considered separately with explicit authorization.
How do we keep improvements after a model change?
Keep the evaluation set, scoring rules, baseline, and known failures under an identified owner. Repeat relevant checks when models, prompts, sources, or tools change. Any rollout needs monitoring and a way to reverse the change if live behavior differs from the test results.