AI integration / Pilot checklist
AI integration for ecommerce: a buyer’s acceptance checklist
Ask for a workflow you can inspect and evaluate.
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Before buying an AI integration, ask to see one defined workflow run against approved examples. Check the answer, the information it used, the human review step, and the cost of handling a request that goes wrong.
Give the pilot one bounded job
A useful first task might be drafting a product description for review, finding an answer in approved documents, or classifying a support request. Write the permitted input and output before choosing a model.
Decide whether the workflow only suggests an answer or can change a business record. Start with reviewable output when mistakes could affect orders, money, or customer commitments. Name the person who approves broader permissions.
Use an acceptance sheet
| Check | Evidence to request | Decision before launch |
|---|---|---|
| Task | Example input and expected useful output | What is inside the pilot's scope? |
| Data | Approved sources and access rules | Which records may the model receive? |
| Quality | Results on ordinary, difficult, and out-of-scope examples | Which errors block release? |
| Review | Demonstration of approve, edit, and reject | Who remains responsible for the result? |
| Cost | Usage, review time, retries, and supporting services | What limit stops uncontrolled spending? |
| Recovery | Fallback behavior and an off switch | How does the team continue without the AI step? |
Make the demonstration difficult enough to be useful
For an invented product-question pilot, test a question answered in the approved catalog, an ambiguous product name, an outdated document, and a request for information outside the allowed sources. Also test a source containing instructions that conflict with the workflow's rules.
Record the expected behavior before running the demonstration. A correct refusal can be a successful outcome when the system lacks evidence or permission. Review the answer and any action separately: a plausible sentence does not prove the proposed order update is correct.
Hold back some examples from development so the final check includes cases the implementation was not tuned against. Choose the volume and acceptance thresholds to match the task's risk. A few polished examples cannot establish dependable performance across a whole business.
Price the workflow around the model
Cloudflare publishes Workers AI pricing by model and usage. Use the current rates with observed request sizes. Include retrieval, storage, application hosting, monitoring, and any other services in the implementation.
Human review belongs in the calculation. In an invented example, reviewing 300 drafts for two minutes each takes ten hours. A low model bill would not remove that workload. Track the percentage of outputs accepted, corrected, rejected, or escalated so the economics reflect the process people actually run.
Inspect a small live example
Devekko's own site uses Cloudflare Workers AI for its project-guide chat and optional brief drafting. The chat provides suggestions; it cannot place orders or send an inquiry for you. That is a bounded example of separating guidance from action, not evidence of results for every AI use case.
Bring one workflow to our AI and data service. A useful initial scope identifies the approved sources, evaluation examples, cost assumptions, and the person who can approve release.
Your next step
Scope one AI workflow
Share the task, your current systems, and the decision you need to make. We’ll discuss a useful first scope.
Scope one AI workflow