Find your store's Agent Visibility Gap.
Cartograph is being built to show which AI agents reach your store, what they do, and where they read, trust, or fail your commerce journey — the evidence your existing tools miss.
Private beta · onboarding design partners now
Built for ecommerce, analytics, product data, security, and checkout teams preparing for AI-mediated shopping.
Catalog freshness · per AI surface
The Agent Visibility Gap is where your tools stop measuring.
The difference between what your current tools can measure and what actually influences AI-mediated discovery, evaluation, cart creation, checkout, and post-purchase outcomes.
- ▲AI systems cannot understand product variants.
- ▲Structured data conflicts with visible page content.
- ▲Product availability is unclear or stale.
- ▲Shipping and return policies are hard for agents to parse.
- ▲Bot controls block useful automation.
- ▲Checkout fails when an agent-like session attempts to validate cart, tax, shipping, or payment.
- ▲Analytics cannot distinguish human, bot, crawler, AI-referred, and agent-assisted activity.
The market has a name for this gap: attribution collapse.
Discovery, comparison, and consideration are moving inside AI agents — off your analytics. The shopper arrives pre-qualified, or never visibly at all, and standard tools can't see the journey that decided the sale. The Agent Visibility Gap, measured across the market, is what's now being called attribution collapse.
AI-referred shoppers now convert better than non-AI traffic — a full reversal from −38% worse twelve months earlier.
Adobe Analytics AI Traffic Report, Apr 2026 · ~1T retail visits
YoY growth in AI retail traffic, Q1 2026
more revenue per visit from AI referrals
global agentic commerce by 2030
›The upside is real and the gap is closing fast. The merchants who can see and attribute agent-mediated traffic capture it; the ones who can't are invisible in their own funnel. Cartograph is being built as the evidence layer intended to make it visible.
Your stack sees the pieces. The planned integration connects the evidence.
Cartograph is not a bot detector, PIM, analytics platform, PSP, or commerce platform. It is being built as the agent-commerce evidence layer across them.
Built for the teams responsible for modern commerce.
One evidence layer, read differently by everyone who owns a piece of the agent-mediated journey.
For ecommerce leaders
The planned evidence views are designed to show where AI-mediated journeys break before cart or checkout.
Learn more →For analytics teams
The v0.1 specification defines evidence labels for verified agents, declared bots, AI-referred activity, and unclassified traffic.
For product data teams
The planned evidence views are designed to surface missing attributes, inaccessible policies, ambiguous variants, and feed gaps that may cause agents to misread products.
For security and bot teams
The planned evidence model is designed to add commerce context without treating bot likelihood as proof of agent identity.
For payments and checkout teams
The planned checkout evidence view is designed to show where agent-like sessions fail across cart, tax, shipping, risk, payment, and confirmation.
Learn more →Is your store ready for agents — answered with evidence, not a checklist.
Because Cartograph is being built to record what agents actually do on your store, the readiness read is designed to go deeper than a surface scan: every Read, Trust, and Transact finding will tie to observed behavior, not just public configuration.
Can agents read your store?
Product data, structured schema, sitemaps, and policies — graded for whether AI agents can actually parse what your customers see.
Do they trust what they read?
Conflicting signals between visible content, feeds, and structured data erode agent confidence and quietly drop you from consideration.
Can they complete the journey?
Bot posture, fraud controls, and checkout surfaces tested against agent-like sessions — so you know where the journey actually fails.
Evidence, not guesswork.
The v0.1 specification defines deterministic traffic labels and evidence codes. It does not infer intent, score actors, or treat missing agent evidence as proof of human traffic.
The v0.1 specification defines five traffic labels: verified agent, declared bot — verified, declared bot — unverified, AI-referred, and unclassified. Those labels describe available evidence at capture; they do not assert that unclassified traffic is human.
The infrastructure for agentic commerce is forming now.
Product feeds, checkout protocols, delegated payments, trusted-agent identity, AI crawler controls, and AI referral analytics are becoming merchant responsibilities. The merchants who prepare early will have cleaner data, fewer checkout failures, and better visibility as AI-mediated shopping grows.
Honest about the scope.
- ×We do not replace your ecommerce platform.
- ×We do not replace your PSP.
- ×We do not replace your PIM.
- ×We do not blindly allow agents to bypass checkout, fraud, or payment controls.
- ×We do not claim every AI crawler is trustworthy.
- ×We do not ask for sensitive customer or payment data for the initial scan.
Questions worth having answered.
- ›Can AI agents understand our products accurately?
- ›Are our product pages and structured data consistent?
- ›Are our policies readable and actionable?
- ›Where would an agent-like journey fail?
- ›Are useful AI agents being blocked by bot controls?
- ›Are suspicious automations being mistaken for legitimate traffic?
- ›Which products are ready for AI-mediated discovery?
- ›What commerce evidence is missing from our analytics?
- ›Where is the economic exposure from agent visibility gaps?
Common questions.
Quick answers about scope and where Cartograph fits.
What is the Agent Visibility Gap?+
The Agent Visibility Gap is the difference between what your current tools can measure and what actually influences AI-mediated discovery, evaluation, cart creation, checkout, and post-purchase outcomes.
What does Cartograph observe?+
Cartograph is being built to observe and evaluate publicly available commerce surfaces first — product pages, structured data, sitemaps, robots and bot posture, return and shipping policies, and selected checkout surfaces. It will not require access to your admin, payment account, or customer data.
Is Cartograph a bot detector, PIM, analytics tool, or PSP?+
No. Cartograph is being built as the agent-commerce evidence layer across them. The planned integration connects evidence from those systems without replacing them.
Who is Cartograph for?+
Ecommerce, analytics, product data, security, and payments teams at merchants preparing for AI-mediated shopping.
See what AI agents see before your customers depend on them.
We're onboarding design partners now. Drop your email and store URL — we'll reach out as early-access opens up.