Local AI search technical SEO: make your business easy to find and verify.

Make the important information accessible first

Technical SEO for local AI search starts with public pages that can be retrieved, understood and connected to the correct business. I would prioritize readable service information, consistent URLs, deliberate crawler settings and truthful structured data. None of these creates a guarantee that ChatGPT, Claude or Google will recommend your company.

For a local business, this is an operating issue as much as a website issue. A customer should be able to establish what you do, where you work, when you are available and how to contact you without assembling the answer from five disconnected places.

My first test is simple: open an important service page on a phone. Can a person answer those questions immediately? Then ask the developer to verify that the page delivers the same useful information to a crawler. A beautiful page that conceals its service details inside an image has left unnecessary work for both.

Give each real service a usable page

I would put the service, relevant coverage area, practical limitations and next step in ordinary page text. Keep essential facts available without a login, a form submission or a sequence of interactions. Use descriptive links from the main navigation, relevant articles and location information so the page is part of the website, not an isolated campaign URL.

Imagine a hypothetical appliance-repair company serving three nearby towns. Its emergency-repair page should explain the appliances supported, actual service coverage and how after-hours requests are handled. If technicians cannot attend every night, the page must say so. An attractive photograph cannot answer that question.

The technical brief should also include useful image descriptions, working mobile navigation and a short, reliable enquiry route. For the editorial side of that work, I would pair this checklist with local website content that answers buying questions.

Keep crawl, indexing and preferred URLs separate

These controls solve different problems. A robots.txt rule manages crawler access. A noindex directive tells a supporting search engine not to index a page. A canonical identifies the preferred version among duplicate or substantially similar URLs. I would ask for a report on all three rather than accepting a single green SEO score.

For public service pages, check that a successful response contains the intended content, no accidental noindex directive remains from staging, and the preferred URL points to the production page. Keep the sitemap and internal links consistent with that preference. A canonical is a signal, not a command that forces a search engine to select the URL.

For a page deliberately excluded with noindex, Google must be able to retrieve the directive; blocking the page in robots.txt can prevent that. Private customer records need access controls, not an indexing instruction. These are different decisions and should have different owners.

In the repair-company example, I would investigate a service page whose canonical still points to the old website before commissioning ten more articles. Fix the contradiction, inspect the deployed response and use Search Console to review what Google can access. Passing a technical check is not evidence that indexing has happened.

Choose AI crawler access deliberately

OpenAI’s crawler documentation distinguishes OAI-SearchBot, used for search, from GPTBot, used for content that may contribute to model training. Their settings are independent. ChatGPT-User handles certain user-initiated visits; OpenAI says robots.txt rules may not apply to those requests. It is not the control for search inclusion.

Anthropic’s crawler guidance similarly separates Claude-SearchBot, ClaudeBot and Claude-User. They serve search, potential training and user-requested retrieval respectively. Anthropic describes controls for each, including the effect of blocking user-requested retrieval.

I would have the business owner choose the access policy, then have the developer check both robots.txt and the hosting or firewall layer. A permissive file does little if the server challenges every legitimate fetch. Allowing search access does not require treating training access as the same decision. Keep private systems protected and verify bot identities using the provider’s current guidance.

Describe the real business; skip invented signals

Google’s LocalBusiness documentation explains the supported properties and recommends the most specific applicable business type. Its rich-result requirements include a business name and physical address. Markup should describe facts people can verify on the site, such as a real location, telephone number and published opening hours.

I would never invent an office address to satisfy a validator, copy another company’s reviews, or add unsupported ratings. A service-area business that does not publish a customer-facing address needs an appropriate implementation, not a fictional storefront. Valid JSON alone does not establish eligibility for a search feature.

Google’s generative AI search guide says foundational SEO remains relevant and that special AI markup is unnecessary. It also says Google Search ignores llms.txt for visibility and ranking. I regard that file as an optional guide for tools that use it, not the centerpiece of a local search investment.

Turn the audit into a short repair queue

I would start with the pages that support the most valuable real enquiries. Give each finding a URL, a specific failure, an owner and a way to confirm the repair. This keeps technical work connected to the customer journey.

• Access: can visitors and intended search crawlers retrieve the public page without an error or challenge?
• Meaning: are service scope, coverage, contact details and limitations readable and current?
• Signals: do canonical URLs, indexing directives, internal links and sitemap entries agree?
• Evidence: does business markup match visible facts, and do tests identify unsupported properties or missing requirements?
• Completion: can someone submit a genuine enquiry, receive confirmation and reach a responsible person?

After repairs, keep a dated record of what changed. Then track useful enquiries alongside discovery, using a practical AI visibility measurement plan. If your team needs help deciding what to fix first, start a visibility conversation with AIM.