You can check it instantly using Revved Digital’s AI Visibility Score Calculator. Just enter your business details, reviews, and traffic numbers. In about 60 seconds you’ll see your AI SEO score plus a forecast of missed leads and revenue if you’re not being recommended by AI search engines.

The AI Visibility Score estimates how likely AI-driven search engines—ChatGPT, Google AI Overviews, Perplexity, Gemini, and similar systems—are to include and recommend your business when someone searches for your services. It’s not a vanity grade. The score reflects six signals that generative engines and local packs lean on:

• Google review volume (0–25 pts, capped at 1,000+) – strong public proof of performance.

• Average star rating (0–20 pts) – trust and safety; sub-3.0 harms inclusion odds, 4.5–5.0 helps.

• Blog/FAQ velocity (0–15 pts) – answer-first, cite-able content that AIs can summarize.

• NAP consistency (0–15 pts) – clean Name/Address/Phone across directories for entity confidence.

• Schema markup (0–15 pts) – machine-readable facts (LocalBusiness, Service, FAQ, Reviews).

• Conversion health (0–10 pts) – realistic uplift grounded in how your site already converts.
  The output is a 0–100 “AI Recommendation Readiness” score plus projected leads, customers, and revenue so you know what improved inclusion could mean in real terms.

Start with a scan using Revved Digital’s AI Visibility Checker. The tool scores your readiness for AI recommendations, highlights gaps in reviews, schema, and content, and estimates how much revenue you may be missing. From there, you can either DIY improvements or request a 90-day visibility plan from Revved Digital.

Your inputs are normalized, scored per signal, and combined into a weighted index (0–100). Two things make the projections useful:

1. Uplift modeling ties discovery gains to your current baseline (monthly sessions and leads). If you already convert 3% of 2,500 visits, we forecast incremental leads using realistic ranges for increased AI inclusion.

2. Revenue modeling multiplies incremental customers by your average customer value and close rate. We keep assumptions conservative; you can tune values later.
   We cap review credit at 1,000 to reduce distortion from outlier counts, scale ratings non-linearly (a 4.9 helps more than a 4.2), credit content cadence only when it’s answer-driven, and treat schema and NAP as entity confidence multipliers. Conversion health moderates fantasy scenarios—great visibility with a weak funnel won’t over-promise.

AEO is the process of making your business visible inside AI-driven answers, not just in blue links. As platforms like ChatGPT, Google AI Overviews, and Perplexity become mainstream, more users are skipping clicks and relying on direct answers. AEO ensures your business information, reviews, schema, and FAQs are structured so these systems can recommend you confidently.

They’re directional forecasts, not fixed guarantees. Accuracy depends on:

• Input quality (accurate traffic, lead counts, value per customer).

• Seasonality and market dynamics (competitors improving, SERP changes, local demand).

• Execution after the audit (reviews, content, NAP, schema actually getting fixed).
  We avoid inflated numbers by tying uplift to your current baselines and using conservative bounds. Treat the outputs like a business case: they show missed revenue and the upside if you improve the exact signals AIs use to recommend local businesses.

• Traditional SEO: rankings for blue links. KPIs: positions, sessions, CTR.

• GEO (Geo-targeted SEO): winning local/city/service and “near me” searches via GBP optimization, citations, reviews, service/city pages. KPIs: map impressions, calls, directions, local leads.

• AI SEO / AEO / LLMO: optimizing your entity so AI systems include you in answers and “recommended providers.” Tactics: structured data, NAP integrity, review strength, answer-first content, distribution on AI-visible surfaces (e.g., Reddit/Quora/industry forums). KPIs: inclusion frequency in AI answers, branded mentions in AI chats, assisted conversions.

Bottom line: shift from ‘rank #1’ to ‘be part of the answer.’ That’s where discovery is heading.

Start with the highest-leverage moves:

1. Reviews and ratings – implement an automated, frictionless review flow; target 1,000+ total and 4.5+ avg; reply to 100% of reviews.

2. Entity clarity – lock down NAP consistency across top directories; ensure GBP categories, hours, services, and photos are complete and real.

3. Schema everywhere it fits – LocalBusiness, Organization, Service, FAQ, Product (if applicable), and Reviews. Validate with a structured data testing tool.

4. Answer-first content – publish short, specific FAQs that address intent directly and cite credible sources. Keep it fresh—quarterly updates beat stale essays.

5. Distribute where AIs look – repurpose your FAQs/answers to surfaces AIs crawl heavily (select forums/Q&A hubs).

6. Fix conversion friction – fast pages, obvious CTAs, click-to-call, appointment links, and trust signals near forms. Visibility without conversion wastes the win.

GEO (Generative Engine Optimization or Geo-targeted SEO, depending on context) focuses on two things: being visible in “near me” searches on maps, and being included in AI-driven generative answers. Traditional SEO chases web rankings; GEO blends entity clarity, reviews, citations, and structured local data to make sure AIs and Google Maps list you when someone nearby searches for your service.

Yes—indirectly and ethically. None of these platforms sell “paid placement” for local businesses. They elevate entities that are well-defined, trusted, and frequently cited. Your AI SEO checker results show exactly which signals are weak. Improve those, and you increase your odds of being mentioned or recommended when users ask for providers in your category and city.
How to verify progress:

• Track referral patterns and branded demand in GA4.

• Use AI-mode channel groupings and regex filters to isolate likely AI-assisted traffic (see your GA4 guide).

• Run consistent test prompts (city + service + qualifiers) and log inclusion frequency over time.

• Monitor GBP insights (calls, direction requests) as leading indicators.

Quarterly is a good minimum. Run an audit:

• After major changes (site redesign, new locations, category shifts).

• During heavy review swings (big volume spikes or rating dips).

• Ahead of peak seasons (HVAC summer/winter, home services rainy season, etc.).
  Multi-location brands should consider monthly spot checks on entity data (NAP/citations/schema) because small inconsistencies multiply at scale and erode AI trust.

The biggest factors include review strength and average rating, structured data (schema), NAP consistency, helpful answer-style content, and entity clarity across the web. These are the trust signals that generative engines lean on when deciding which business is “safe” to recommend.

It’s built for local and service-led businesses (plumbers, HVAC, electricians, med-spa/salon, restaurants, gyms, pro services, multi-location brands) that want calls, bookings, and foot traffic—not just pageviews. If your score shows weak entity clarity, thin answer content, or poor review signals—and you don’t have time to fix it—bring us in. We’ll translate your score into a 90-day execution plan:

• Clean NAP + citations, GBP overhaul, review engine.

• LocalBusiness/Service/FAQ schema, answer-first content, and city/service coverage.

• Distribution to AI-visible surfaces and monthly AI-readiness tracking tied to leads and revenue.