The agentic-commerce readiness standard, measured live across six AI engines
Agentic Commerce Readiness for www.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 www.adamsilvaconsulting.com.
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 agentic commerce consulting 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
166msyour 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.
07
What outside sources say about you
Strong: Outside sources cover you above average for your category.
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.
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 pages2
Earned media mentions8
Independent pressyes
Social profiles0
Business footprint
Case studies4 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 www.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.
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.
