How to make Claude recommend your business

You cannot make Claude recommend a business on command. You can make the business easier to evaluate by publishing specific, consistent and independently supported evidence about who it serves and why it fits a buyer's requirements. The aim is not to appear in every answer. It is to become a defensible option for the questions where the company is genuinely qualified.

Define the recommendation you deserve

Start with a narrow statement: customer type, job to be done, market and constraints. "Recommended accounting software" is too broad. "Accounting software for a five-person Norwegian agency that needs bank reconciliation and accountant access" creates criteria Claude can evaluate. Translate positioning into 20 to 40 prompts spanning discovery, comparison and validation. Exclude use cases the product cannot support so visibility does not outrun reality. Rank prompts by commercial importance and confidence that the business is eligible. This keeps an attractive but irrelevant broad query from receiving more attention than questions that qualified buyers actually ask.

Publish evidence for the decision criteria

• Create focused product or service pages that state audience, availability, core capabilities and meaningful limitations.
• Make integrations, locations, languages, security claims and support policies explicit rather than implied.
• Use descriptive headings and concise answers, followed by detail a buyer can verify.
• Keep price and eligibility information current, or clearly explain how a prospect obtains an accurate quote.
• Show dates or versions where capabilities change, especially in documentation and comparison pages.

Strengthen source agreement

Claude may encounter your website, business profiles, documentation, reputable directories, editorial coverage and reviews. Contradictions make confident recommendations harder. Audit sources you control for old names, discontinued products, wrong locations and stale positioning. Seek legitimate independent evidence where it helps customers assess the business. Do not buy fabricated reviews, manufacture citations or publish anonymous "best" lists disguised as editorial coverage.

Make the site technically understandable

Important evidence should be available in accessible HTML, connected through useful internal links and permitted by the crawler policy you actually intend. Structured data can clarify entities and page meaning when it matches visible content, but it does not guarantee inclusion. Use the schema generator as a starting point and the schema validator to catch implementation errors.

Measure recommendations honestly

1. Save a fixed prompt set and record the model, date, locale and web-search setting.
2. Separate an unprompted recommendation from a mention caused by putting the brand in the question.
3. Score fit and accuracy alongside recommendation rate; a confident but unsuitable recommendation is not a win.
4. Record competitors and citations to understand what evidence shaped the shortlist.
5. Repeat prompts over time because generated answers vary and model behavior changes.

ModelSaid helps teams test high-intent questions, review answer context and compare the businesses Claude names. Scan your current AI visibility before changing pages, then group gaps by missing evidence, inconsistent facts, technical access or weak third-party corroboration. Coverage can expand with the AI answer landscape, allowing the same commercial questions to remain the stable unit of analysis. Estimate the customer harm and commercial value of each gap, name an owner, and retest the original question after the source change has had time to become available.

No markup, phrase density or submission form can guarantee that Claude recommends a company. Recommendations are generated in context and can differ by model, prompt and date. Focus on being clear, credible and appropriately visible. The strongest program improves the information available to buyers even when no AI assistant is involved, while monitoring confirms whether those improvements are reflected in real answers. Review negative or conditional recommendations rather than trying to suppress them: they may expose a real limitation the company should communicate more plainly. Honest disqualification can protect trust and improve lead quality. Close the loop with customer-facing teams. If Claude repeatedly highlights a criterion you do not address, decide whether the product should meet it, the website should clarify it or the business should accept that another provider is a better fit. Record that decision beside the prompt. This prevents marketing from chasing visibility that would create poor-fit inquiries, and it turns answer monitoring into useful market research rather than a narrow exercise in brand insertion.