What does machine-native mean for a cannabis website?
A technical definition based on explicit information, bounded interfaces, and evidence—not a promise of rankings or automated sales.
Technical guideAIWebSignals research library
Machine-native commerce.
Evidence before claims.
Practical research for regulated websites: make public information understandable, keep authority explicit, and measure what actually happens. Cannabis is the initial sector focus.
12 published guides · Original methodology and clearly labeled synthetic examples · No benchmark findings claimed
Start with the fundamentals
Age gates and crawler evidence: what a public audit can establish
Distinguish a necessary eligibility boundary from an observation limitation without impersonating a crawler or bypassing access controls.
Technical guideRobots.txt permission is not evidence of an AI visit
A practical evidence model for declared crawler policy, actual responses, verified requests, and downstream outcomes.
Technical guideAI referral traffic and crawler traffic are different datasets
How to avoid treating automated requests as customers or attributing an outcome to a crawler without evidence.
Technical guideDesign useful, bounded interfaces
What llms.txt can—and cannot—establish
Use an optional machine-readable guide to clarify resources without confusing a proposal with guaranteed discovery.
Technical guideWhen a regulated website needs an agent interface—and when it does not
A decision framework for public information retrieval, authenticated observations, and explicit authority boundaries.
Technical guidePublic readiness is not permission to expose customer data
Design a machine-readable public surface without leaking operational records, credentials, or confidential identities.
Technical guideObserve machine traffic without taking custody of admin credentials
Separate public scans, site-local collectors, and connected evidence so observation does not require unrestricted access.
Technical guideEvaluate claims and publish evidence
How to audit an AI-readiness claim
A repeatable review of scope, evidence, freshness, uncertainty, and optional capabilities.
Technical guideHow to benchmark regulated websites without inventing certainty
A study design that preserves sample boundaries, confidentiality, unknowns, and reproducible observations.
Technical guideRegistry listed, protocol compatible, actually used: three different milestones
Apply MachineRealms’ interoperability lessons without treating directory presence as demand or verification.
Technical guideHow to learn from a private implementation without identifying the business
A confidential pilot methodology using generic lessons, reviewed abstractions, and synthetic examples—not a named case study.
Technical guideUseful methods, not invented results.
The library distinguishes policy from observed access, crawler requests from human referrals, and a protocol listing from completed participation. Read the methods now; future benchmark studies will be published only after evidence collection and review.
Put the method to work
Check one public signal. Choose one next step.
The free scanner inspects supported public signals. It does not complete every check in this research methodology or prove actual AI traffic. Do not scan a confidential site to create public evidence.
No email gate. No scan target or report identity is attached to these links. A research handoff is not a submitted contribution.