The agentic-commerce readiness standard, measured live across six AI engines
Agentic Commerce Readiness for greenstreethvac.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 Green Street HVAC, LLC.
Generated Aug 17, 2026Category HVAC installation and repair 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 Green Street HVAC, LLC, trust it, and complete a purchase.
42
/ 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
167ms
against the 300ms target
Revenue exposed
$66K/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
35
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 HVAC installation and repair services companies
No organic mention across the measured buyer prompts.
Not surfaced organically
Claude
60
claude-sonnet-5
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Aug 17, 2026
Perplexity
46
sonar
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Aug 17, 2026
Copilot
30
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 HVAC installation and repair 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
Claude
60
Estimated from the current ACRA score model until live receipts are available.estimated
Perplexity
46
Estimated from the current ACRA score model until live receipts are available.estimated
ChatGPT
35
Estimated from the current ACRA score model until live receipts are available.estimated
Copilot
30
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.
Thin structured data - ChatGPT cannot extract your products, pricing, or answers.
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
Unverified brand entity - Claude cannot confirm who you are without linked, corroborated identity.
Missing structured data - Claude reads your page as unlabelled text.
No case studies or documented outcomes for Claude to weight.
Highest-leverage fix
Unverified brand entity - Claude cannot confirm who you are without linked, corroborated identity.
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.
No research citations or authorship depth for Perplexity to trust.
No external validation for Perplexity to reference.
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). Insufficient schema - Gemini relies on it more than any other engine.
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
Insufficient schema - Gemini relies on it more than any other engine.
No Google Business Profile / sameAs links - breaks Gemini entity disambiguation and AI Overviews.
Weak E-E-A-T - Gemini will not treat you as an authoritative source.
Highest-leverage fix
Insufficient schema - Gemini relies on it more than any other engine.
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.
No earned media or fresh public chatter for Grok to surface.
No dated, recent content - Grok heavily favours recency.
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 HVAC installation and repair 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
167msyour 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.
02
How clearly AI can understand your business
Currently limiting your visibility: 10 of the labels AI looks for are on your site; 10 important ones are missing.
06
Whether AI can confirm you exist across the web
Currently limiting your visibility: 5 social profile(s) detected. AI uses these to confirm you exist as a real business.
07
What outside sources say about you
Currently limiting your visibility: Some outside coverage. Building more reviews and press will move this signal fast.
09
How often AI is recommending you to buyers today
Mixed: AI assistants recommend you in roughly 49 of every 100 buyer queries. Inconsistent across prompts.
04
Whether AI tools use your content in their answers
Mixed: AI tools include you in some answers, miss you in others.
08
How trusted you look to AI overall
Mixed: AI sees you as a real business but not yet as a category leader.
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.
03
Whether AI search engines quote your site
Solid: 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 types10
Press and media
Self-published pages0
Earned media mentions0
Independent pressnone
Social profiles5
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 greenstreethvac.com stands and the exact gap to close.
Modeled monthly revenue exposed as buyers shift to agent-driven discovery.
$66Kper month at risk
Where the modeled exposure concentrates
48%
15%
14%
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.
Insufficient Structured Data AI agents cannot read your product data in machine-readable format. Pricing, availability, and specifications are invisible to agentic buyers.
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.
LLMs cannot verify your business identity, funding status, or contact details. This causes hallucinations when AI answers questions about your company.
No Product/Service Schema
AI agents cannot read your pricing, availability, or product specifications. Your catalog is invisible to agentic commerce flows.
Unverified Brand Entity
AI systems cannot confirm your brand identity. Without a complete Organization schema linked to social profiles, Wikipedia, and directories, LLMs treat your brand as an unverified entity — leading to hallucinations and missed recommendations.
Cannot Accept Agentic Purchases
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.
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 high.
"
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.
443
pages audited
of 443 discovered
96
average page score
100%
pages with structured data
3/11
root manifests present
Authority signals across your content (not just the homepage)
Measured across 434 content pages, so a bare homepage no longer hides the work your articles already do.
Named author100%
Publication date100%
Article markup100%
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.
1,499 dollars per year for the premium annual subscription.
299 dollars per 10 scans for the 10 looks pack.
Monthly service plans are preferred after the assessment. Your ACRA fee is credited toward ASC services when you proceed. Affiliate discount codes are available.
