Evaluation dataset and acceptance rules
Understand the current setup and agree the decisions this part of the work depends on.
AI Evaluation, Monitoring & Cost Controls
Make changes to an AI feature measurable through representative tests, useful monitoring and visible usage.
AI Evaluation, Monitoring & Cost Controls
Define casesRun & investigateVerify & report
Illustrative workflow. No customer records, traffic figures or test results are shown.
THE WORK
What this engagement can include
This service focuses on ai evaluation, monitoring & cost controls. We agree the exact platforms, deliverables and acceptance criteria before implementation, so the proposal describes a bounded piece of work.
Quality and failure reporting
Implement the agreed behavior in a suitable environment and share it for review.
Usage budgets and regression checks
Exercise the important paths and document the result, limits and next operating steps.
A POSSIBLE ENGAGEMENT
How this could work in practice
A support assistant changes its model or retrieval settings. The team compares answers on approved test cases before rollout.
This is an illustrative scenario, not a claim about a completed client project.
EVIDENCE & HANDOVER
How we check the work
Repeatable runs of the same dataset before and after a change, with results, latency and cost recorded.
What you receive at handover
The agreed implementation or assessment, the relevant verification evidence, known limitations and instructions for the tasks your team will own. Access and source ownership, third-party licenses and any ongoing support are identified in the proposal.
See our project process →What we need from you
The feature under test, representative requests, expected outcomes and access to usage or application logs.
A named decision maker helps resolve questions, approve the scope and review work at the agreed checkpoints.
What we agree separately
An evaluation cannot prove that a model will never fail. Security, domain expertise and manual review requirements are scoped separately.
Requirements discovered during the work are reviewed before they are treated as additional deliverables. Timelines and fees follow the approved scope.
YOUR QUESTIONS
Before we get started
How do you decide whether an AI answer is good?
We agree task-specific criteria such as factual support, completeness, permissions and correct escalation. Where judgment is subjective, domain reviewers help label the evaluation cases.
What should we share in the first conversation?
The feature under test, representative requests, expected outcomes and access to usage or application logs. If those details are incomplete, start with the problem, the current setup and the outcome you need. We can identify what must be clarified before quoting the work.
How will scope, timing and acceptance be agreed?
We review the requirements and dependencies, propose deliverables and milestones, and define who approves the result. For this engagement, verification includes: Repeatable runs of the same dataset before and after a change, with results, latency and cost recorded.
CONNECTED SERVICES
Explore related work
Need a different part of ai & workflow automation? These are separate scopes that can be combined when the project requires them.
AI & Workflow Automation
Start with what needs to work better.
Tell us about your current setup, the problem and any deadlines. We’ll discuss whether this service is the right fit before proposing the work.
Discuss your project