A useful audit should explain what is holding a site back and what to improve next. AI Search Helper turns public website signals into a practical plan instead of a pile of disconnected warnings.
Ten checks, one connected view
The scan combines crawlability, question coverage, entity clarity, structured data, trust, citation readiness, topical depth, prompt coverage, competitor context, and a prioritized roadmap. Scores are directional evidence—not a promise of inclusion in any AI answer.
- Up to 20 important HTML pages per site
- Clear passed checks, warnings, and recommended fixes
- Optional comparison with as many as three competitors
- JSON and CSV exports plus a print-friendly report
Signals that reflect how answers are assembled
Answer engines need accessible pages, unambiguous entities, extractable explanations, and supporting evidence. The tool looks for these building blocks together so a schema win cannot hide thin content, and strong writing cannot hide blocked crawling.
Recommendations your team can route
Every roadmap item includes the issue, why it matters, a suggested page, expected impact, and difficulty. Technical work can go to developers while missing questions and topic gaps can go directly to writers.
AI crawler accessibility and technical discovery
The technical audit checks whether valuable pages can be discovered and retrieved without obvious barriers. It reviews robots.txt availability, XML sitemap signals, page status codes, meta robots directives, canonical URLs, redirect behavior, crawl depth, failed pages, and the optional llms.txt file. These checks help surface contradictions such as an indexable-looking page that declares noindex, a sitemap filled with redirects, or a canonical that points away from the content being evaluated. Because crawler policies differ, the report presents access observations rather than claiming that any specific AI platform will collect or use a page.
Answer engine optimization and question coverage
The AEO content analysis looks beyond keyword frequency. It identifies question-led headings, concise definitions, short answer passages, instructional steps, summaries, FAQs, comparison language, and problem-solving explanations. The goal is to reveal whether a reader can find a complete response near the question or must assemble meaning from scattered marketing copy. Suggested questions are based on the visible subject, products, services, audience, and likely search intent. They should be reviewed by a knowledgeable editor before publication so every answer remains accurate and useful.
Entity, schema, and brand knowledge signals
AI systems need to distinguish the organization from its products, authors, locations, and topics. The entity review checks visible business descriptions, contact details, About content, service language, authorship, locations, and consistent naming. The structured data review then inventories JSON-LD types such as Organization, LocalBusiness, Person, Article, Product, Service, WebSite, FAQPage, and BreadcrumbList. Schema is treated as supporting context, not a shortcut: recommendations should reflect facts already visible to visitors and meet the eligibility rules of the search products where they may be used.
Trust, citations, and authority evidence
The trust and citation checks look for the practical signals that help a person evaluate a source: named authors or reviewers, contact routes, policy pages, original research, transparent methods, primary references, case studies, meaningful dates, limitations, and correction practices. A citation-ready page normally makes a specific claim, explains its scope, shows where the evidence came from, and distinguishes observation from opinion. The audit cannot verify whether a testimonial, certification, or statistic is true, so it flags visible patterns and leaves factual validation to the site owner.
Exports for content, SEO, and development teams
The completed report can be printed or saved as a PDF and exported as structured JSON or CSV. Agencies can move high-priority technical findings into development tickets, route missing questions to content strategists, and give schema tasks to the appropriate implementation owner. In-house teams can use the crawled URL list to identify thin or failed pages and use the prompt table to map informational, commercial, comparison, local, transactional, and problem-solving searches to a clear destination. The report is designed as a planning artifact, not an automated publishing system.