But an answer is an observation, not a diagnosis. The useful next step is to examine the question, the sources, and the information a buyer can actually find on your site.

I'm working on AI Cite Who, a tool that helps website owners investigate these recommendation gaps and turn the evidence into practical website work. Here's the process behind it, including what the evidence can and cannot tell you.

1. Start with a real customer task

A brand-name search can show whether an assistant recognizes your name. It does not answer a different question: whether your product appears when a buyer describes a problem without naming you.

Write down a small set of questions grounded in actual use cases and constraints. For a fictional client-feedback tool, for example:

• What can replace long email threads for design reviews?
• Which feedback tools let clients comment without creating an account?
• How can a small agency collect approvals from external clients?

Each question describes a different decision. Only use examples that match your actual product and audience; verify the facts before you claim your product fits.

2. Record the answer and distinguish the evidence

For each question, save the platform, date, question, answer, and brands mentioned. Then look closely at any sources available:

• Final-answer citation: a source linked in the answer itself.
• Retrieved candidate: a potential search lead, which does not by itself prove the final answer used it.
• Checked page fact: something you can verify on the public page you inspected.
• Hypothesis: a possible explanation or improvement that still needs testing.

Keeping these separate matters. A missing mention in one answer does not prove that a specific page caused the omission. Answers can also vary across platforms, runs, and contexts. If a check fails or evidence is unavailable, record it as unknown rather than treating it as a negative result.

3. Turn a finding into a reviewable task

Suppose an answer to the fictional client-account question does not recommend your product, and the product page you inspected does not explain whether clients need accounts. That is a useful information gap to investigate, not proof of why the assistant omitted you.

A practical task might look like this:

1. Confirm the real account requirements with the product team.
2. Explain the client access steps and any applicable plan restrictions on the relevant public page.
3. Check that a prospective buyer can find and understand the explanation.
4. Save the change and re-check the same question and platform later.

The result is a concrete improvement to customer-facing information. Even if a later AI answer changes, that observation alone cannot establish that the edit caused the change.

4. Use a repeatable workflow

AI Cite Who combines public website analysis with separate recommendation audits on selected available ChatGPT, Gemini, and Perplexity platforms. The reports organize question outcomes, available source evidence, and suggested website or external actions. Plus and Pro users can also download a Markdown handoff with page plans, required facts, and completion checks for review with an AI coding tool.

The free plan includes one website project and one public website analysis every seven days. AI recommendation audits are a separate Plus or Pro feature and use credits. You can inspect the current scope and costs on the pricing page.

If you own a SaaS product, an independent brand, or a service business and want a clearer starting point, you can start a website analysis. Review the findings against your own product knowledge, implement only changes that are accurate and useful, and keep the original questions so future checks are comparable.

No tool or edit can guarantee an AI recommendation. The goal is to make decisions from observable evidence and give both people and search systems clearer, verifiable information.

• Start from a tight spec: one input group, one output, no accounts. If I can't describe it in two sentences, it's too big.

• Let the assistant generate the first cut (HTML + a little JS, or a tiny framework if state gets messy). I review the math, not the boilerplate.

• Hardcode the formulas and test edge cases myself (negative numbers, empty input, unit conversions). AI is great at structure, bad at catching a sign error in a physics formula.

• Deploy to a static host, point a domain at it, done.

The part AI doesn't help with: deciding whether anyone wants it. That only shows up after you put it in front of the community it's for. I've learned to ship, then listen.