A client report should make it easy to understand what was observed, why it matters and what to review next. A percentage without its question set or sample size makes that difficult, especially when AI answers can vary between observations.
ProtoScrape can supply structured Google AI Mode answers and available citations for your reporting workflow. Your agency builds the schedule, analysis and report in its own tools. Here is a practical outline that keeps the evidence visible without overwhelming the reader.
Begin with the scope, in plain language
State the engine, dates, market, language, device and number of questions. Separate branded questions from category discovery and comparison questions. Explain how you selected them: for example, from a client-approved list of product questions or recurring customer concerns.
Describe the result as a study of that question set. Do not call it the client’s share of all AI search traffic. ProtoScrape currently supports Google AI Mode in US English on desktop; Google Search, ChatGPT and Perplexity are still in validation. A report should name only the engines actually measured.
Use an overview that leads back to evidence
Show successful observations, collection failures, brand mention counts and cited-domain counts. Include denominators next to percentages. If a metric changed from the previous period, show whether the same questions and settings were used.
A compact report can use the following structure:
| Report section | What the reader should learn |
|---|---|
| Scope and method | Which questions and conditions were measured |
| Collection coverage | How many observations succeeded or failed |
| Mentions and citations | Where the brand or domain appeared in the sample |
| Example observations | The exact query, answer, sources and date |
| Review priorities | What information deserves investigation next |
Do not hide a failed question behind an average. If failures change the topic mix, explain the gap and avoid drawing a strong conclusion from the period comparison.
Include a few inspectable examples
Choose examples that explain the pattern, including cases where the brand did not appear. Preserve the original answer and source links in your internal evidence store. In the report, show the relevant excerpt with enough context to avoid changing its meaning, and link back to the cited pages.
Keep a job ID and collection timestamp with every saved observation. ProtoScrape results are retained for approximately seven days, so export them promptly if your client work needs a longer audit trail. Label any sample data used in a template as illustrative before showing it to a client.
Convert findings into specific review tasks
If answers repeatedly misunderstand a product feature, compare the answer with the client’s public documentation. If relevant citations point to detailed comparison pages, review whether the client’s own material answers the same customer question clearly and accurately.
Write a concrete task such as “clarify which plans include role-based permissions” rather than promising that a content edit will force inclusion in an AI answer. Record the change date and continue the same observation method. A later improvement is useful evidence to investigate, but does not by itself prove causation.
Keep the process repeatable
Version your question list, document brand aliases and domain groupings, and save metric definitions alongside each report. Keep client datasets separated in your own reporting system. Reuse idempotency keys only for retries of the same intended job, not for a fresh reporting period.
ProtoScrape provides API collection, job tracking, keys and credit usage. Scheduled client reports, exports to a particular reporting platform and custom dashboards are workflows you implement around the API. Start with the integration guide, then use the visibility measurement guide to define a method your clients can understand.