Find your store's Agent Visibility Gap.
Cartograph is being built to scan your product pages, structured data, policies, bot posture, and checkout surfaces — and show where AI agents can read, trust, or fail your commerce journey.
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
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.
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.
Your stack sees the pieces. Cartograph connects the evidence.
Cartograph is not a bot detector, PIM, analytics platform, PSP, or commerce platform. It is 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
See where AI-mediated journeys break before they reach cart or checkout.
For analytics teams
Classify AI-referred, crawler-like, bot-like, human, and unknown commerce activity with evidence labels.
For product data teams
Find missing attributes, inaccessible policies, ambiguous variants, and feed gaps that may cause agents to misread products.
For security and bot teams
Separate useful automation from suspicious automation using commerce context, not just bot likelihood.
For payments and checkout teams
Understand where agent-like sessions fail across cart, tax, shipping, risk, payment, and confirmation.
Evidence, not guesswork.
Cartograph does not guess where precision matters. We separate observed evidence from inferred signals, unknowns, and unresolved issues so teams know what to fix, what to investigate, and what to monitor over time.
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 will Cartograph scan?+
Publicly available commerce surfaces first — product pages, structured data, sitemaps, robots and bot posture, return and shipping policies, and selected checkout surfaces. The initial readiness scan 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 the agent-commerce evidence layer across them. Your existing tools see pieces of the journey — Cartograph connects the evidence.
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.