Recommendation Experiments for AI-assisted vendor research

Buyers increasingly use consumer AI assistants and AI search experiences to research vendors. The answer they receive can depend on whether the system can resolve a company correctly, understand its category, find reliable supporting information, compare it with alternatives and justify a recommendation.

WonderApricot studies what those external systems show buyers. We capture how selected AI systems respond to defined B2B buying scenarios and document their answers and visible sources. We then diagnose an observable knowledge or evidence gap and turn it into one bounded experiment.

This makes the work more specific than tracking mentions. A Recommendation Experiment turns observation into a decision about one intervention: test it, evaluate what changed, then scale, change or stop.

A Recommendation Experiment is a structured before-and-after test of one defined change to the public knowledge, evidence or source environment around a B2B provider.

It begins with a clear commercial question and a fixed set of realistic buyer scenarios. WonderApricot records the first responses produced under consistent, documented conditions, including visible citations and sources where available. We then diagnose a specific gap, form a testable hypothesis, verify the facts involved and define one bounded intervention.

After the intervention is implemented or published and has had a reasonable opportunity for public retrieval, the same scenarios are tested again under the closest feasible conditions. The comparison shows the observable direction of change, its limits and the next decision. It does not turn a small before-and-after result into a universal claim.

A Recommendation Experiment can examine observable questions such as:

• Does an AI system identify the company correctly rather than confuse it with another entity?
• Does it place the company in the right service or product category?
• Does it cite sources that visibly support what it says?
• Does it include the company in a relevant comparison?
• Does it shortlist or recommend the company under a defined buyer scenario?
• What knowledge, evidence or source weakness does the response explicitly reveal?

These are measured observations from selected systems and conditions. They are not guarantees about every model, buyer, market or future response.

WonderApricot is not an AI visibility dashboard that stops at mention counts, share of voice or prompt tracking. Those observations can be useful inputs, but monitoring alone is not a Recommendation Experiment.

It is not a generic SEO or GEO reporting service. Search accessibility, structured information and source quality may matter to an intervention, but the work is organized around a question defined in advance and a comparable retest, not rankings, traffic or content volume.

It is not internal evaluation of a customer's LLM, product A/B testing, AI governance consulting or recommender-system optimization. WonderApricot observes external consumer AI systems used by buyers. It does not test or modify the customer's own model, and it does not change a third-party recommendation algorithm.

WonderApricot is for B2B companies whose buyers increasingly use AI assistants to research and compare vendors.

It is most relevant when the company needs more than a visibility score: it wants to understand an observable recommendation problem, choose one prioritized change and learn whether comparable responses differ afterward.

1. 1Define the decision. Agree on the buyer question, target company and observable outcome that matters.
2. 2Observe the baseline. Run frozen buyer scenarios on selected external consumer AI surfaces and preserve the first responses and visible sources.
3. 3Diagnose the gap. Separate entity, category, knowledge, evidence and source problems using what the responses actually show.
4. 4Form and verify a hypothesis. Choose one plausible intervention and verify the facts it will contain.
5. 5Implement one bounded change. Publish or complete the defined intervention while holding other deliberate changes as stable as practical.
6. 6Retest comparably. Repeat the same scenarios after a documented period of public availability, recording surface changes and limitations.
7. 7Evaluate the result. Compare the preserved observations and decide whether to scale, change or stop.

The result is an evidence-backed decision based on observable AI responses, citations, sources and controlled comparisons. WonderApricot does not claim access to hidden model reasoning. A change observed after an intervention is not automatically proof that the intervention caused it.

Negative and null results are valid: they can prevent further investment in an unsupported idea or show that a different gap should be tested next.

WonderApricot does not guarantee inclusion, citation, shortlisting, recommendation or a particular commercial outcome.

WonderApricot is a specialist AI knowledge and recommendation research service built around Recommendation Experiments for B2B companies.

It studies how external consumer AI systems interpret public information when buyers research, compare, shortlist and evaluate providers.