The agentic-commerce readiness standard, measured live across six AI engines
Agentic Commerce Readiness for mrgateshvac.com
AI shopping agents are already choosing companies for buyers in this market. Not by ranking pages, by whether they can find you, trust you, and complete a purchase. This grades exactly that for Mr. Gates Heating and Cooling.
Generated Aug 17, 2026Category heating and air conditioning servicesProtocols audited UCP, ACP, AP2
ACRA verdict
D
Not ready for AI-led buying.
This is the hard grade for whether an AI agent can find Mr. Gates Heating and Cooling, trust it, and complete a purchase.
54
/ 100
AI VisibilityCritical
0/100
4 of 6 engines surfaced the business organically.
Can an agent buy from you?No
1/4 protocols
Engines measured
2/6
live, with receipts
Engines that found you
4/6
organic model visibility
Agent parse budget
479ms
against the 300ms target
Revenue exposed
$45K/mo
modeled on agentic adoption
0A Receipts from the live measurement
Every engine row shows the prompt, model version, date, and evidence used for the score.
ChatGPT
40
gpt-5.6-sol
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Aug 17, 2026
Gemini
0
gemini-3.6-flash-high
Asked best heating and air conditioning services companies
No organic mention across the measured buyer prompts.
Not surfaced organically
Claude
65
claude-sonnet-5
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Aug 17, 2026
Perplexity
69
sonar
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Aug 17, 2026
Copilot
60
gpt-5.6-sol
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Aug 17, 2026
Grok
0
grok-4.5
Asked best heating and air conditioning services companies
No organic mention across the measured buyer prompts.
Not surfaced organically
1 AI Visibility across the six engines
When a buyer asks an AI engine to recommend a company, does it name you? We measure every engine and label estimated fallbacks.
0
Measured average
Perplexity
69
Estimated from the current ACRA score model until live receipts are available.estimated
Claude
65
Estimated from the current ACRA score model until live receipts are available.estimated
Copilot
60
Estimated from the current ACRA score model until live receipts are available.estimated
ChatGPT
40
Estimated from the current ACRA score model until live receipts are available.estimated
Gemini
0
No organic mention across the measured buyer prompts.measured
Grok
0
No organic mention across the measured buyer prompts.measured
2 of 6 engines measured live: gemini-3.6-flash-high, grok-4.5 on Aug 17, 2026
1A What each engine needs
Each engine weights different evidence. These are the specific requirements, gaps, top fix, and score rationale for this report.
ChatGPT
not measured
Lowest remaining engine score
Not measured this run (this engine is not connected yet). This is not a zero; the priorities and gaps below still apply.
No agentic protocol layer (UCP/ACP) - ChatGPT cannot transact with or confidently surface you.
No verified reviews or press for ChatGPT to validate trust.
Highest-leverage fix
No agentic protocol layer (UCP/ACP) - ChatGPT cannot transact with or confidently surface you.
Claude
not measured
Not measured this run (a temporary measurement error). This is not a zero; the priorities and gaps below still apply.
What it prioritizes
Entity clarity (Organization schema linked via sameAs)
Demonstrable E-E-A-T and real outcomes (case studies)
Clean structured data and agent infrastructure
Your gaps
No case studies or documented outcomes for Claude to weight.
Highest-leverage fix
No case studies or documented outcomes for Claude to weight.
Perplexity
not measured
Not measured this run (the AI provider account hit its usage limit). This is not a zero; the priorities and gaps below still apply.
What it prioritizes
Citable third-party sources (Perplexity is citation-first)
Press coverage and research citations
E-E-A-T and review trust
Your gaps
No citable press footprint - Perplexity ranks by third-party sources it can cite.
Highest-leverage fix
No citable press footprint - Perplexity ranks by third-party sources it can cite.
Microsoft Copilot
not measured
Not measured this run (this engine is not connected yet). This is not a zero; the priorities and gaps below still apply.
What it prioritizes
Same OpenAI backbone as ChatGPT, plus the Microsoft business graph
LinkedIn and professional authority signals
Syndicated press and structured data
Your gaps
Weak LinkedIn / professional presence - Copilot leans on the Microsoft business graph.
No agentic protocol layer - Copilot shares ChatGPT's execution requirements.
No syndicated press for Copilot to weight as third-party authority.
Highest-leverage fix
Weak LinkedIn / professional presence - Copilot leans on the Microsoft business graph.
Gemini
0/100
Gemini did not name you in any buyer query we asked (score 0/100). No Google Business Profile / sameAs links - breaks Gemini entity disambiguation and AI Overviews.
What it prioritizes
Rich schema markup (Gemini is the most schema-hungry engine)
Google Business Profile and Google-native signals
E-E-A-T and social sameAs for entity disambiguation
Your gaps
No Google Business Profile / sameAs links - breaks Gemini entity disambiguation and AI Overviews.
Highest-leverage fix
No Google Business Profile / sameAs links - breaks Gemini entity disambiguation and AI Overviews.
Grok
0/100
Grok did not name you in any buyer query we asked (score 0/100). No active X / social presence - Grok is built on the live X signal; this is its #1 input.
What it prioritizes
Live X / social presence and recency (its single biggest input)
Active public conversation and earned media
Fresh, dated content
Your gaps
No active X / social presence - Grok is built on the live X signal; this is its #1 input.
Highest-leverage fix
No active X / social presence - Grok is built on the live X signal; this is its #1 input.
2 Where the AI purchase breaks
Every protocol an agent needs to buy from you has to be discoverable before the transaction can start.
!
UCP
Discover
Agent cannot find what you sell
!
ACP
Check out
Agent cannot complete a purchase
!
AP2
Verify trust
No mandate, enterprise AI walks
Purchase blocked
1 of 4 protocols detected
3 The buyer you never see
This is the moment that is already happening in your market, today.
Buyer asks AI
best heating and air conditioning services near me
AI names 3 companies
the ones it can actually read
You are not one of them
the buyer calls a competitor
You never see this happen, and you never see the sale you lost
4 Too slow for an AI to read
An agent waits about 300 milliseconds before it gives up and reads a faster competitor.
300msagent budget
479msyour load
0ms800ms1600ms
5 The nine signals AI reads
The technical pillars remain the source of truth, translated here into buyer-facing language.
01
Worst failing signal
Whether AI shopping assistants can buy from you
Currently limiting your visibility: AI shopping assistants cannot buy from you cleanly today. They can browse the site, but the signals they need to transact are missing.
07
What outside sources say about you
Needs work: Some outside coverage. Building more reviews and press will move this signal fast.
06
Whether AI can confirm you exist across the web
Mixed: 6 social profile(s) detected. AI uses these to confirm you exist as a real business.
10
How cheaply AI can read your pages
Mixed: Machine readability is mixed. Raw facts exist, but script weight or markup noise still taxes agent parsing.
09
How often AI is recommending you to buyers today
Solid: AI assistants recommend you in roughly 63 of every 100 buyer queries. Strong signal.
04
Whether AI tools use your content in their answers
Solid: AI tools include you in their answers.
08
How trusted you look to AI overall
Solid: AI treats you as a trusted business in your category.
02
How clearly AI can understand your business
Strong: 21 of the labels AI looks for are on your site; 7 important ones are missing.
03
Whether AI search engines quote your site
Strong: AI search engines can quote you cleanly.
05
Whether AI crawlers can reach your site at all
Strong: AI crawlers can reach your site cleanly.
5A What AI found out about you
Independent data points the scan pulled from sources beyond your own site. These are the facts AI engines cross-check before they trust you.
What AI found out about you
6 data points pulled from independent sources, not your own site
Authority and identity
Wikipedianone
Schema.org types21
Press and media
Self-published pages0
Earned media mentions0
Independent pressnone
Social profiles6
5B Third-party reviews AI can verify
Reviews are the trust layer AI engines check before recommending you. These are your live counts and the gap to each platform's bar.
Third-party reviews AI can verify
AI shopping engines trust businesses real customers have reviewed. Some commerce surfaces require review depth before they trust a merchant. Here is where mrgateshvac.com stands and the exact gap to close.
Modeled monthly revenue exposed as buyers shift to agent-driven discovery.
$45Kper month at risk
Where the modeled exposure concentrates
70%
19%
11%
No Agentic Protocol (UCP/ACP/AP2) AI shopping agents cannot discover or purchase from your site. You are invisible to the fastest-growing commerce channel.
No Press / PR Coverage Press releases are the #1 fast-track to AI authority. Without them, your brand authority score stagnates as competitors get cited.
Weak Social Authority Social proof is an LLM trust signal. Low social authority reduces AI recommendation frequency by up to 40%.
The plan
Your prioritized path to recommended
Every gap above, ordered by what to fix first. Close these and AI engines move you from invisible to the company they name.
1
Fix first
Critical gaps blocking AI from transacting with or trusting you
No UCP Profile
Your site has no UCP profile at /.well-known/ucp (the official Google path) or the legacy /.well-known/ucp/manifest.json. AI shopping agents cannot discover your product catalog.
ChatGPT Instant Checkout, Claude Commerce, and Perplexity Shop require ACP-compatible endpoints. Without this, AI agents literally cannot buy from you.
No /agent.json or /ai-plugin.json means LLMs cannot load your capability schema for function calling or tool use.
Low Text-to-HTML Efficiency
HTML-to-visible-text ratio is 214:1. AI agents spend too many tokens parsing markup and boilerplate before reaching useful facts.
Heavy Script Hydration Tax
64 script tag(s), 33 external script reference(s), and 159051 script byte-weight units were detected in raw HTML. Heavy JavaScript raises the token and execution cost for agents.
No SpeakableSpecification Schema
Voice AI and LLM answer engines cannot identify which content to read aloud or cite as authoritative answers.
Methodology note. Scores combine protocol checks, machine-readable signals, the nine ACRA pillars, and live model receipts when available. Missing measurements degrade to labeled estimates.
Machine-readability context. Crawlability grade A, zero-click net loss 22%, hallucination risk medium.
"
By 2028, ninety percent of B2B purchases will run through AI agents. The engines that cannot see you will not ask permission, they will simply recommend someone else.
Gartner via Digital Commerce 360: agent-mediated purchases
Each signal is scored against the routine questions a buyer in your category would actually ask an AI assistant.
Buyer queries
240 routine questions an AI assistant fields for a buyer in your category, tested across 5 AI platforms.
Per-signal score
How often you surface cleanly versus how often a similar peer does on the same query.
Versus peers
The peer median is the middle competitor in your category at the time of the scan.
Full methodology available on request
Full-site deep dive
Every page, audited the way an AI agent reads it
We followed your sitemaps and root manifests to find every page your site publishes, then audited each one and wrote the specific fix for it. This is the part a one-page scan cannot see.
170
pages audited
of 170 discovered
90
average page score
100%
pages with structured data
3/11
root manifests present
Authority signals across your content (not just the homepage)
Measured across 149 content pages, so a bare homepage no longer hides the work your articles already do.
Named author99%
Publication date99%
Article markup99%
Publisher markup100%
Root manifest health
The agent-protocol and crawler-guidance files checked at your web root, the way an AI agent would.
ACRA measures whether agents can find you, trust you, and complete a purchase. Review the gaps with our team, then keep rescanning as fixes ship and competitors move.
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