SAMPLE - sanitized for demonstration
Page 1 of 5 · Cover
How AI tools see your business today.
ABC Roofing & Exteriors
Roofing & Exterior Restoration · Elk Grove Village, IL · abcroofing.example
AI Visibility Score
47/100
GRADE C
Mixed Signals
AI tools can find your business but often recommend competitors first. Real-world reputation is strong; public digital signals are inconsistent. Closing 3 to 4 specific gaps would move you from 'sometimes recommended' to 'consistently recommended' in local AI search.
SAMPLE - sanitized for demonstration
Page 2 of 5 · Scorecard
How AI engines score your public footprint.
Six categories are reviewed independently. This public sample shows two sections fully and redacts the remaining detail; the full client version includes the complete analysis without exposing scoring weights.
Business Clarity
Sample section shown
Service-Area Visibility
Sample section shown
Competitor Comparison
Redacted in public sample
Trust Signals
Redacted in public sample
Technical Signal Gaps
Redacted in public sample
AI Discoverability
Redacted in public sample
Executive Read
AI tools can find your business but often recommend competitors first. Real-world reputation is strong; public digital signals are inconsistent. Closing 3 to 4 specific gaps would move you from 'sometimes recommended' to 'consistently recommended' in local AI search.
The pages that follow walk through each category in plain English, name the specific gaps, and end with a ranked roadmap of the three highest-leverage moves.
SAMPLE - sanitized for demonstration
Page 3 of 5 · Findings
What we found, in plain English.
Each finding describes the gap, why it matters for AI search, and the public evidence behind it. No platform access or CRM data was used.
Business Clarity
AI tools can identify the business name, industry, and primary service. However, the list of specific services (Roofing vs Siding vs Restoration vs Driveways vs Painting) is presented as inline page copy rather than a structured service catalog. Some AI engines parse this correctly; others surface only the dominant service.
What we saw
Homepage title and meta description name the business clearly. About page describes a family-owned, multi-decade operation. Service pages exist but lack consistent structured data describing each service line.
Service-Area Visibility
Geographic clarity is one of the largest gaps. AI tools verify a single city in the structured data, but the business serves a broader regional area. Without that explicit signal, an AI answering 'Who is a reliable roofer near Schaumburg?' may not surface this business even when it operates there daily.
What we saw
Only one city is explicitly declared in the public structured business data. No service-area or neighborhood pages exist on the website. Reviews mention multiple suburbs by name, but those mentions aren't surfaced in a way AI engines can verify.
Trust Signals
This is the highest-impact gap. Customer reviews exist on Facebook and word-of-mouth is strong locally, but Google Business Profile reviews are sparse and infrequent. AI engines weight verified review volume heavily when deciding who to recommend in local trades.
What we saw
Reviews are concentrated on a single platform (Facebook) where AI tools have limited verification access. No AggregateRating schema is exposed on the business website. No proactive review-request workflow is in place, so the pipeline of new public reviews is unpredictable.
SAMPLE - sanitized for demonstration
Page 4 of 5 · Competitors + Technical
Competitor Comparison
Three regional competitors in the same Northwest Chicagoland roofing market are easier for AI engines to recommend, not because their work is better, but because their public footprints are more legible. Each runs a Google Business Profile with consistent service categories, complete hours, named service areas, and a steady review pulse.
Competitor A
- Service-area page per suburb
- Active review acquisition (12+ in last 90 days)
- Schema.org Service catalog with 4 distinct entries
Competitor B
- Long-form FAQ page indexed for AI Q&A retrieval
- Consistent NAP across 15+ citation sources
- Owner profile linked across multiple regional directories
Competitor C
- Strong YouTube channel with embedded customer testimonials
- Listed in 3 Chicagoland 'best of' regional roundups
- Detailed pricing-range disclosures that AI engines treat as trust signals
Technical Signal Gaps
The website is on a hosted builder platform that ships a baseline LocalBusiness schema but leaves several signals incomplete or missing entirely.
Specific gaps
- Telephone, opening hours, and structured service catalog missing from the public schema
- Two competing H1 headings on the homepage create ambiguity about the primary topic
- No FAQPage schema, despite the team answering the same five questions on most sales calls
- Canonical URL and robots meta directives are absent
- No conversion analytics installed, so there is no way to measure the lift from any fix
AI Discoverability
The business already has an llms.txt file in place, which is ahead of most competitors in this category. However, the file lists pages without context, and the public business description across the web is inconsistent across the website, Facebook, and the few directories where the business appears.
What we saw
llms.txt present and well-structured. Conflicting business descriptions across the website footer, Facebook About section, and Google Business Profile reduce how confidently AI engines can repeat the brand story.
SAMPLE - sanitized for demonstration
Page 5 of 5 · Roadmap
The three highest-leverage moves, in order.
Ranked by impact-per-effort. You can run these yourself, hand them to an existing agency, or have us scope the work. The structure is the same either way.
1
Activate a structured review-acquisition workflow
Impact: High
Effort: Low
Why it mattersTrust signals are the heaviest weighted input to local AI recommendations. Closing the review-pulse gap moves the needle faster than any other single fix.
OutcomeA predictable monthly cadence of new public reviews on the platforms AI engines verify against, instead of relying on Facebook alone.
2
Publish dedicated service-area pages for the suburbs you actually serve
Impact: High
Effort: Medium
Why it mattersAI tools answering geographically scoped questions need an explicit signal for each suburb. Service-area pages plus matching structured data tell every major AI engine which neighborhoods to surface this business in.
OutcomeRecommendation surface area expands from a single city to the full regional service footprint without changing operations.
3
Complete the structured business data on the website
Impact: Medium
Effort: Low
Why it mattersThe hosted builder ships partial structured data. Filling in the missing fields (telephone, hours, service catalog, FAQ) is straightforward but materially improves how cleanly AI engines parse the business.
OutcomeAI engines repeat the brand story consistently and surface specific services (roofing vs siding vs restoration) instead of collapsing the business into one category.
Founder's Note
This Snapshot was hand-reviewed by me personally before it was sent. I'm not a software company; I'm a one-on-one operator helping local businesses get found in AI search. If any of the findings above don't match what you know about your own market, tell me. I'd rather get it right than be polished and wrong.
Ray · Founder, AiBizTune
Your next move is yours to choose.
Option 1
Do nothing.
You now have a plain-English read on where you stand and the three highest-leverage moves. That's yours to keep, with or without us.
Option 2
Fix it yourself.
If you have an in-house person or an existing agency, hand them this Snapshot. The roadmap is structured so anyone competent can execute it.
Option 3
Have us run the fixes.
If you'd like AiBizTune to scope and run the roadmap above, reply to the email this Snapshot arrived in, or book a 15-minute Discovery Call from the website. No pressure, no surprise invoices.