The specific process behind every VisiGap AI Visibility Audit: what we scan, how we simulate AI queries, how we evaluate 83 parameters, and how we benchmark against local competitors — in 48 hours.
Every VisiGap audit runs the same four-step process: citation source scan (48–52 sources), AI query simulation (5 queries × 3 engines = 15 responses), 83-parameter diagnostic (schema, content, local, trust, and conversion signals), and competitor benchmarking (top 3 local competitors, same framework). Output: a prioritized gap report with fixes ranked by business impact, delivered within 48 hours.
AI engines do not generate local business recommendations from scratch. They ground recommendations in structured data from specific, verifiable sources. VisiGap scans every source in our category-specific library — checking for presence, accuracy, and consistency across each one.
This is the highest-weighted component of the AI Visibility Score (25 points) because it is the primary mechanism AI engines use to verify a business's identity, category, and location before citing it. A business absent from key sources is a business AI cannot verify. A business AI cannot verify is a business it will not cite.
VisiGap selects 5 queries for each business based on its primary service category and city. Each query is run live across ChatGPT, Google AI Overviews, and Perplexity — 15 total responses evaluated per audit. We record whether the business is cited, whether a named competitor is cited instead, and whether the business appears in any context relevant to the query.
The query set is not arbitrary. It mirrors the actual query patterns AI users ask for local services — derived from verified SERP behavior data across 237,000+ home service queries (WebFX 2025) and legal/healthcare query studies (Whitespark Q2 2025, BrightEdge Dec 2025).
Every audited business is evaluated across 83 parameters covering the five signal categories AI engines use to determine whether a local business is worth citing. Parameters are weighted by their impact on AI citation likelihood, not by traditional SEO value.
The framework separates parameters into two tiers: Preview (the 20 highest-impact parameters that drive the headline findings in the report) and Full Audit (all 83 parameters, fully scored and ranked by fix priority). Every $499 audit delivers the full 83-parameter evaluation.
VisiGap identifies the top three local competitors for each audited business using the AI query simulation results: whichever businesses appear most frequently in AI-generated responses to the simulated queries are the primary competitive benchmark. We then evaluate each competitor against the same seven-component AI Visibility Score framework.
The competitor analysis answers one question: what specific signals does the most-cited competitor have that the audited business does not? That gap — not the absolute score — is what determines fix priority in the report.
AI engines ground local business recommendations in structured data from specific sources. VisiGap's source library is organized into five categories. The exact count varies by industry because different service categories have different authoritative source sets.
Neustar Localeze, Data Axle, Foursquare, Factual. These four aggregators supply business data to the majority of secondary directories and are the primary identity verification layer AI engines use.
Google Business Profile, Apple Maps, Bing Places, Facebook, Yelp, BBB, Nextdoor, Yellow Pages. These are the highest-authority citation sources for local businesses across all categories.
Avvo and FindLaw for legal; Healthgrades and Zocdoc for healthcare; Houzz and Angi for home services; Psychology Today for behavioral health. These vary by category and carry the highest weight for AI citations in their respective verticals.
MapQuest, Citysearch, MerchantCircle, Superpages, CityLocal, EZlocal, and approximately 12 others. These reinforce the primary record and increase AI citation confidence through source volume.
Google Reviews, Yelp, Facebook Reviews, Trustpilot, Birdeye, and 1–3 industry-specific review sources. Review count, recency, and rating all influence AI citation confidence — particularly for healthcare and legal categories.
Example query set for an HVAC contractor in Chicago. Every query set is generated specifically for the business's service category and city. Each query runs live across all three AI engines at audit time — not from a cached database.
Query 1 ("best HVAC company in Chicago") triggers AI Overviews only 12% of the time — local pack still dominates. Queries 2, 3, and 4 trigger AI Overviews 37–41% of the time. This is the actual risk: AI is now answering the mid-funnel questions your website used to answer before a customer decided to call. If your business is not cited in those AI responses, the customer who was researching before calling you has already moved on to a competitor that was cited.
Five parameter categories, each weighted by AI citation impact. This is not a traditional SEO audit — parameters are weighted by how much they influence AI engine citation decisions, not by how much they affect Google rankings.
The primary mechanism by which AI systems identify, classify, and cite a local business. Without correct schema type, AI cannot determine what service the business provides. Without NAP in structured data, AI cannot verify location. Without entity-specific schema, AI cites the wrong category or not at all.
AI systems cite pages that answer questions well. Thin service pages, absent FAQ content, and missing educational context are the most common reasons a business is skipped in AI answers. FAQ presence alone is a near-decisive signal — 78% of home service businesses have none.
AI local answers depend on the system's ability to confirm that a business serves a specific location. City name in page title and H1 are non-negotiable. Businesses that omit their primary city from key content positions are effectively invisible to location-qualified AI queries.
E-E-A-T (Experience, Expertise, Authoritativeness, Trust) is an explicit factor in AI quality assessment. For healthcare and legal businesses, named practitioners with visible credentials are required for AI systems to treat the site as authoritative. This weight rises to 15%+ for healthcare and legal categories.
AI crawlers face the same access barriers as Google — a page blocked in robots.txt cannot be cited. Mobile speed and a working booking flow determine whether an AI-generated referral converts to an actual customer contact. A significant share of AI-referred visitors arrive outside business hours.
Every VisiGap audit delivers the same set of outputs, formatted for immediate use: you hand it to whoever you already work with, or implement the top three fixes yourself. No follow-on pitch. No retainer required.
Your composite score across all seven components, with individual component scores shown separately. Compared directly to the top three local competitors in your category.
Every source scanned. Status for each: present and accurate, present with errors, absent. Errors show the exact discrepancy — what your record says vs. what it should say.
Results from all 15 query-engine combinations. Which responses cited your business. Which cited a competitor. The exact competitor name and why it was cited instead.
Full parameter-by-parameter evaluation with weighted scores, sorted by fix impact. Each failing parameter shows exactly what is wrong and exactly what to change.
Side-by-side comparison with your top three local competitors across all seven AI Visibility Score components. Shows the specific signals your most-cited competitor has that you do not.
Three to five specific fixes, ranked by impact on your AI Visibility Score, with implementation steps. Designed to hand directly to a web developer, marketing manager, or agency without additional translation.
VisiGap runs the full four-step methodology on your business: 48–52 sources scanned, 5 queries simulated, 83 parameters evaluated, 3 local competitors benchmarked. Prioritized fix report in 48 hours.
Order My Audit — $499