One Business. Two Questions. Different Evidence.

The company stays the same. The customer need changes.

Consider an illustrative business offering both AI training and process automation. Its profile contains service descriptions, workshop experiences, implementation cases and credentials. Two customers ask different things of that profile.

This walkthrough uses invented example records to explain selection. It is not a live comparison of AI providers and does not report measured improvements in mentions, rankings or enquiries.

What belongs in each evidence pack?

For training, the strongest useful context may include the audience’s starting level, examples of practical tasks, workshop delivery and participants’ experience. A detailed integration case is less central unless the question also asks about implementation.

For automation, system compatibility and delivery evidence become more important. A general review praising a workshop does not establish experience building a production workflow.

Each pack should retain its sources and distinguish business-supplied descriptions from customer experiences and specific confirmations. It should also identify unknowns. Selection changes emphasis; it must not change what a source actually says.

Try the question change yourself.

The interactive sample profile lets you change the question and inspect a different evidence selection. All records in that demo are illustrative.

Use it to ask a practical question about your own business: do you have evidence for each service you want to be chosen for, or does most of your proof describe only one part of your work?

How to turn this into a real, repeatable test.

1. Choose a company and obtain permission to use its public evidence.
2. Write a fixed set of realistic customer questions before running the comparison.
3. Create two inputs: a general business description and a service-linked evidence pack. Document differences in both content and length.
4. Keep the model, instructions, search setting and test period as consistent as possible.
5. Repeat each condition and save the full outputs, supplied evidence and retrieved source URLs.
6. Check each factual claim against its source and publish the complete method alongside the results.

A controlled prompt comparison tests the effect of the supplied information in that setting. An open-web discovery test asks whether the assistant finds the business itself. They answer different questions and should be reported separately.

Measure the explanation, not only the mention.

Record whether the company was mentioned, whether it was presented as suitable, whether the reasons match the question, and whether the supporting claims are accurate and traceable. Record unsupported claims and missing important constraints too.

Keep a citation separate from an endorsement. If you later measure enquiries, explain the observation period and how referrals were attributed. Until a test has been run, publish the method and demonstration as such, without invented before-and-after percentages.

Make the next question easier to answer.

Start by listing two different reasons customers choose your business. Identify which evidence supports each reason and which facts still need updating or collecting.

This is the practical foundation of Honeyguide’s approach: connect the question, your expertise and the proof. Use the business profile checklist to build those relationships, or read how the evidence is delivered.