Live assessment: six AI engines queried when available
Agentic Commerce Readiness for www.adamsilvaconsulting.com
AI shopping agents are already choosing companies for buyers in this market. This measures whether they can find, trust, and recommend Adam Silva Consulting.
Generated Jun 27, 2026Category agentic commerce and AI optimization consultingProtocols audited UCP, ACP, AP2
B
84 / 100
Technical Readiness Grade
Strong infrastructure
Your infrastructure score. Strong technical readiness still does not guarantee AI recommends you, that is the AI Visibility number beside it.
AI VisibilityCritical
0/100
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
319ms
against the 300ms target
Revenue exposed
$8K/mo
modeled on agentic adoption
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 27, 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
Not measured this run (the AI provider account hit its usage limit). This is not a zero; the priorities and gaps below still apply.
Gemini did not name you in any buyer query we asked (score 0/100). Weak E-E-A-T - Gemini will not treat you as an authoritative source.
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
Weak E-E-A-T - Gemini will not treat you as an authoritative source.
Highest-leverage fix
Weak E-E-A-T - Gemini will not treat you as an authoritative source.
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). No research citations or authorship depth for Perplexity to trust.
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 research citations or authorship depth for Perplexity to trust.
Highest-leverage fix
No research citations or authorship depth for Perplexity to trust.
Microsoft Copilot
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
Same OpenAI backbone as ChatGPT, plus the Microsoft business graph
LinkedIn and professional authority signals
Syndicated press and structured data
Grok
0/100
Grok did not name you in any buyer query we asked (score 0/100). No dated, recent content - Grok heavily favours recency.
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 dated, recent content - Grok heavily favours recency.
Highest-leverage fix
No dated, recent content - Grok heavily favours recency.
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 and AI optimization 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
319msyour load
0ms800ms1600ms
5 The nine signals AI reads
The technical pillars remain the source of truth, translated here into buyer-facing language.
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.
03
Whether AI search engines quote your site
Strong: AI search engines can quote you cleanly.
04
Whether AI tools use your content in their answers
Mixed: AI tools include you in some answers, miss you in others.
05
Whether AI crawlers can reach your site at all
Strong: AI crawlers can reach your site cleanly.
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.
09
How often AI is recommending you to buyers today
Strong: AI assistants recommend you in roughly 72 of every 100 buyer queries. Strong signal.
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.
6 What it is costing you
Modeled monthly revenue exposed as buyers shift to agent-driven discovery.
$8Kper month at risk
Where the modeled exposure concentrates
100%
Poor Generative Engine Optimization LLMs are bypassing your content for competitors with stronger E-E-A-T and topical cluster architecture.
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 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
Your next step
Get a 30-minute call with our team.
On the call, we walk through the 3 gaps that matter most for your business, why each one is costing you visibility, and the order to fix them. No proposal pressure, no slide deck. Just a clear next step you can act on whether or not we end up working together.
