Evaluate → Diagnose → Verify™ AISearch.wiki uses a three-step visibility workflow: First → Evaluate whether your page has strong AI visibility potential, Second → Diagnose whether technical search-readiness issues could hold it back, Then → Perform the final observed check to see whether your brand actually appears—and which competitors appear instead.
Evaluate discoverability, entities, evidence, answer extractability, trust and freshness.
Inspect crawlability, indexing, canonicalization, structured data and crawlable content.
Run defined prompts, capture mentions and citations, and compare tracked competitors.
Each checker answers a different question. Use them in sequence so the final observed result has context: first determine whether the page appears citation-worthy, then find technical blockers, then verify what AI answer systems actually return.
Question: Should this page have strong potential to appear and be cited?
Inspect public signals that can make a page easier to discover, understand, trust and use as a source. This diagnostic measures potential—not current proprietary AI results.
Question: Is anything technical preventing stronger AI-search visibility?
Audit the technical and on-page foundation that search and AI systems need before they can reliably reach, index and interpret a public page.
Question: After evaluating and diagnosing the site, does your brand actually appear—and who appears instead?
Run a defined prompt set across configured providers, distinguish mentions from domain citations, and compare the same observations against tracked competitors.
AISearch.wiki evaluated 40 prominent Miami personal injury law firms using its 100-point AI Visibility Potential framework. The September 2026 benchmark found strong overall discoverability—but a much larger gap in citation-ready evidence.
Headline finding: Miami firms were generally highly discoverable, averaging 23.1/25 for Discoverability, but substantially weaker at publishing citation-ready evidence. No firm in the 40-firm cohort reached the 90+ Excellent tier.
These scores measure observable public website and source signals—not legal quality and not current proprietary AI-platform ranking positions. Actual mentions and citations should be measured separately with defined prompts using the Observed AI Visibility Checker.
VIEW THE MIAMI RESEARCH → DOWNLOAD THE DATA →| Measurement | Question | Method | Best use |
|---|---|---|---|
| 01 · AI Visibility Potential | Does this page contain strong signals for understanding, trust and citation-worthiness? | Evaluate discoverability, entities, evidence, answer structure and trust signals. | Establish the page's visibility potential and prioritize improvements. |
| 02 · AI Search Readiness | Can search and AI systems technically reach and interpret this page? | Inspect the public webpage and discovery controls. | Find crawl, index, canonical, schema and structural blockers. |
| 03 · Observed AI Visibility | Does the brand or domain actually appear for the prompts being tested? | Observe returned answers and sources across configured providers. | Measure mentions, citations, prompt coverage and changes over time. |
| 03B · Competitor Comparison | Who surfaces more often for the same prompt set? | Compare tracked brands under the same observation framework. | Identify visibility gaps and competitive opportunities. |
The first two tools explain the site's potential and technical foundation. The third tool is the final reality check: whether the target actually surfaces for the prompts being tested.
Measure entity clarity, evidence, answer structure, discoverability, trust and freshness.
Find crawl, indexing, canonical, crawler-access, schema and structural obstacles.
Run the final check: measure actual mentions, citations, prompt coverage and competitor share of voice.
Once the observed checker establishes a baseline, recurring monitoring becomes the subscription value: track visibility changes, identify competitor gains, improve the underlying site, and rerun the same prompt framework to measure what changed.
AI-search optimization starts with ordinary search fundamentals, then adds clearer entities, stronger evidence, self-contained answers and measurement of what actually surfaces.
AI-search visibility begins with a page that can be crawled, indexed and understood as part of the broader web. Fix ordinary technical-search problems before searching for an AI-only shortcut.
Use stable HTTPS URLs, valid status codes, deliberate robots directives, useful internal links and canonical signals. Google states that the same foundational SEO practices remain relevant to AI Overviews and AI Mode and that there are no special additional technical requirements solely for those features.
A strong page makes it obvious who or what it is about, how named entities relate to each other, and which organization or author is responsible for the information.
Use descriptive titles and headings, consistent brand naming, accurate structured data that matches visible content, authorship where appropriate, and supporting identity pages. Structured data can improve machine understanding, but it is not an AI-ranking switch.
Citation-worthy content should make important claims inspectable. Show the source, methodology, date, first-hand test, original dataset or other basis when the claim needs evidence.
Original analysis and primary evidence give a page a reason to be used instead of merely paraphrased. For time-sensitive facts, show when the information was checked and update it when conditions change.
Put a direct answer near the beginning of an important section, then support it with detail, examples, comparisons and evidence.
Question-oriented headings, readable tables, lists and self-contained paragraphs make content easier for people to scan and easier for retrieval systems to interpret. Avoid hiding key information only inside images or client-side interactions.
Readiness and page quality are not the same as observed visibility. Test defined prompts and record which brands, domains and sources actually appear.
Keep the prompt set stable when comparing changes. Note the provider, time and any location assumptions. Observed AI answers are variable, so one result should not be treated as a permanent universal ranking.
When a competitor appears and you do not, use the gap as a research question: what source, entity, content, authority or technical difference could explain the observation?
Strengthen the underlying page, publish genuinely useful evidence, improve internal discovery and retest the same prompt set. Track meaningful changes rather than optimizing for a single screenshot.
Different products can select different sources for the same question. Treat each system as its own observation surface rather than assuming one result represents every AI answer product.
Observed AI visibility measures whether a brand or domain actually appears, is mentioned or is cited for a defined set of prompts at a defined time. It is evidence from an observation, not a permanent ranking.
The checker applies the same prompt set and observation rules to your target and tracked competitors, then compares mentions, citations, prompt coverage and approximate share of voice. This helps reveal who is surfacing more often for the same questions.
Visibility Potential inspects public website signals such as entities, sources, answer structure and trust. Observed Visibility checks returned AI answers and sources to see whether the brand actually surfaced for defined prompts.
AI Search Readiness is the technical foundation: crawlability, indexability, canonicalization, readable HTML, structured data and related signals that help search and AI systems reach and interpret a page.
No. Search and AI platforms use proprietary systems, and results can vary by query, provider, locale, personalization, time and available sources. AISearch.wiki scores and observations are diagnostic evidence, not guarantees.
No guaranteed ranking effect should be assumed. Treat llms.txt as an optional publishing and discovery convention. Crawlability, indexing, useful content, strong entities, evidence and ordinary SEO fundamentals remain more important.
OpenAI states that publishers should allow OAI-SearchBot if they want site content to be discoverable for ChatGPT search summaries and citations. Crawler access is only one prerequisite; it does not guarantee selection.
Last updated: September 9, 2026. AISearch.wiki distinguishes observable website diagnostics from actual platform observations and does not claim to reverse-engineer proprietary ranking systems.