Shared ACRA report for adamsilvaconsulting.com · generated June 29, 2026 · Get your own ACRA report
A94
adamsilvaconsulting.com captured Jun 29, 2026
The agentic-commerce readiness standard, measured live across six AI engines
Agentic Commerce Readiness for adamsilvaconsulting.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 adamsilvaconsulting.com.
Generated Jun 29, 2026Category businessProtocols audited UCP, ACP, AP2
ACRA verdict
A
AI agents can find, trust, and buy from this business.
This is the hard grade for whether an AI agent can find adamsilvaconsulting.com, trust it, and complete a purchase.
Verified AI company
94
/ 100
AI VisibilityCritical
0/100
2 of 6 engines surfaced the business organically.
Can an agent buy from you?Yes
4/4 protocols
Engines measured
4/6
live, with receipts
Engines that found you
2/6
organic model visibility
Agent parse budget
311ms
against the 300ms target
Revenue exposed
$0/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
100
gpt-5.5
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Jun 29, 2026
Gemini
0
gemini-3.5-flash-low
Asked best business companies
No organic mention across the measured buyer prompts.
Not surfaced organically
Claude
0
claude-sonnet-4-6
Asked best business companies
No organic mention across the measured buyer prompts.
Not surfaced organically
Perplexity
0
sonar
Asked best business companies
No organic mention across the measured buyer prompts.
Not surfaced organically
Copilot
75
gpt-5.5
Asked heuristic fallback
Estimated from the current ACRA score model until live receipts are available.
Estimated: Jun 29, 2026
Grok
0
grok-3-fast
Asked best business 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
ChatGPT
100
Estimated from the current ACRA score model until live receipts are available.estimated
Copilot
75
Estimated from the current ACRA score model until live receipts are available.estimated
Gemini
0
No organic mention across the measured buyer prompts.measured
Claude
0
No organic mention across the measured buyer prompts.measured
Perplexity
0
No organic mention across the measured buyer prompts.measured
Grok
0
No organic mention across the measured buyer prompts.measured
4 of 6 engines measured live: gemini-3.5-flash-low, claude-sonnet-4-6, sonar, grok-3-fast on Jun 29, 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 (the request timed out). This is not a zero; the priorities and gaps below still apply.
Not measured this run (the request timed out). 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
Gemini
0/100
Gemini did not name you in any buyer query we asked (score 0/100). Build the signals this engine relies on.
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
Claude
0/100
Claude did not name you in any buyer query we asked (score 0/100). Build the signals this engine relies on.
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
Perplexity
0/100
Perplexity did not name you in any buyer query we asked (score 0/100). Build the signals this engine relies on.
What it prioritizes
Citable third-party sources (Perplexity is citation-first)
Press coverage and research citations
E-E-A-T and review trust
Grok
0/100
Grok did not name you in any buyer query we asked (score 0/100). Build the signals this engine relies on.
What it prioritizes
Live X / social presence and recency (its single biggest input)
Active public conversation and earned media
Fresh, dated content
2 Where the AI purchase breaks
Every protocol an agent needs to buy from you has to be discoverable before the transaction can start.
ok
UCP
Discover
Protocol detected for agents
ok
ACP
Check out
Protocol detected for agents
ok
AP2
Verify trust
Protocol detected for agents
Purchase enabled
4 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 business 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
311msyour load
0ms800ms1600ms
5 The nine signals AI reads
The technical pillars remain the source of truth, translated here into buyer-facing language.
10
Worst failing signal
How cheaply AI can read your pages
Strong: Strong machine-readable HTML. Agents can reach substantive facts without excessive script or markup overhead.
09
How often AI is recommending you to buyers today
Strong: AI assistants recommend you in roughly 83 of every 100 buyer queries. Strong signal.
06
Whether AI can confirm you exist across the web
Strong: 32 social profile(s) detected. AI uses these to confirm you exist as a real business.
07
What outside sources say about you
Strong: Outside sources cover you above average for your category.
08
How trusted you look to AI overall
Strong: AI treats you as a trusted business in your category.
03
Whether AI search engines quote your site
Strong: AI search engines can quote you cleanly.
01
Whether AI shopping assistants can buy from you
Strong: Strong setup.
02
How clearly AI can understand your business
Strong: 44 of the labels AI looks for are on your site; 1 important ones are missing.
04
Whether AI tools use your content in their answers
Strong: AI tools include you in their answers.
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
9 data points pulled from independent sources, not your own site
Authority and identity
Verified entityai tool
Wikidata entityQ138829358
Wikipedianone
Schema.org types0
Press and media
Self-published pages10
Earned media mentions17
Independent pressyes
Social profiles0
Business footprint
Case studies3 5 named clients
Named clients found
Walmart + UCP: Enterprise AI Agent Commerce at ScaleTarget + UCP: Product Feed Standardization for AI AgentsMCP: From Anthropic Experiment to 97M+ Monthly SDK DownloadsSchema.org + AEO/GEO: 3.2x Higher AI Citation RateRAG-Powered Customer Service: Enterprise Resolution at Scale
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 adamsilvaconsulting.com stands and the exact gap to close.
Modeled monthly revenue exposed as buyers shift to agent-driven discovery.
$0per month at risk
Where the modeled exposure concentrates
8%
8%
8%
Lost discovery AI never surfaces you
Lost trust ranked below rivals
Zero-click answered without a visit
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.
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 10%, hallucination risk low.
"
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
Your next step
Keep the report current while AI buying changes.
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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