# AIWebSignals

> Evidence-based technical readiness for public websites and agent discovery surfaces. AIWebSignals does not promise rankings, citations, traffic, or business outcomes.

## Tools

- [AI Website Readiness Scanner](https://aiwebsignals.com/app): Scan a public website for crawler access, discovery surfaces, structured data, and metadata.
- [AI Activity Radar](https://aiwebsignals.com/ai-activity-radar): Explore public agent services and their published discovery records with explicit evidence labels.

## Guides

- [AI Crawler Access Guide](https://aiwebsignals.com/guides/ai-crawler-access): Understand which public technical controls affect AI crawler access and how to verify the result.
- [robots.txt for AI Crawlers](https://aiwebsignals.com/guides/robots-txt-ai-crawlers): Write clear robots.txt policies for named AI crawlers without confusing access controls with guarantees.
- [llms.txt Explained](https://aiwebsignals.com/guides/llms-txt): Use llms.txt as an optional discovery aid while keeping expectations grounded in observable evidence.
- [Structured Data Readiness](https://aiwebsignals.com/guides/structured-data-readiness): Expose important entities and page meaning with valid, consistent structured data.
- [Sitemaps and Discovery Surfaces](https://aiwebsignals.com/guides/sitemaps-discovery): Create a reliable XML sitemap and connect it to the public signals machines use to find content.
- [Metadata Readiness Guide](https://aiwebsignals.com/guides/metadata-readiness): Align titles, descriptions, canonical URLs, language, indexing directives, and social metadata.

## Site information

- [About](https://aiwebsignals.com/about): Purpose, evidence model, safety principles, and current capabilities.
- [Privacy](https://aiwebsignals.com/privacy): Current data, analytics, advertising, and notification practices.
- [Terms](https://aiwebsignals.com/terms): Terms and acceptable use.
- [Our Network](https://aiwebsignals.com/our-network): Related independent consumer, technology, creative, and digital projects.
- [Contact](https://aiwebsignals.com/contact): Support, feedback, privacy, and security channels.

## Methodology

- Public registry records and Agent Cards are public evidence, not traffic measurements.
- Site-specific observed traffic requires owner-authorized first-party analytics.
- Scanner downloads are bounded and downloaded page bodies are not stored.
- [Agent Journeys](https://aiwebsignals.com/agent-journeys): Local-first first-party AI request path and workflow reconstruction.
- [Connect AI Traffic](https://aiwebsignals.com/connect-ai-traffic): Connect supported first-party AI traffic sources for continuous analysis.
- [AI Impact](https://aiwebsignals.com/ai-impact): See AI traffic patterns, probable journeys, friction, and access-policy opportunities.
- [Agent Policy](https://aiwebsignals.com/agent-policy): Set, test, and apply machine-access rules using observed first-party traffic.
- [AI Revenue](https://aiwebsignals.com/ai-revenue): Set prices for licensed machine access, use x402 payment requirements, and measure successfully settled machine-access revenue.
- [Machine Web Research](https://aiwebsignals.com/research): Evidence-first research on AI crawlers, generative search, agent readiness, access policy, referrals, and paid machine access.
- [AI Bot Index](https://aiwebsignals.com/research/ai-bot-index): Purpose-first index of major AI crawler identities and publisher controls.
- [Research Methodology](https://aiwebsignals.com/research/methodology): How AIWebSignals separates observed evidence from inference and economic claims.
- [Pricing](https://aiwebsignals.com/pricing): Free, Pro, and Business plans for AI traffic intelligence and machine-access controls.
- [Advertise](https://aiwebsignals.com/advertise): Sponsorship opportunities across AIWebSignals Research and Guides.
- [Humans & Machines](https://aiwebsignals.com/research/humans-and-machines-internet): A layered explanation of how humans and machines encounter information, data, networks, permissions, and value differently.

## Machine API
- [Machine discovery](https://aiwebsignals.com/.well-known/aiwebsignals.json): Stable entrypoint for programmatic discovery.
- [Capabilities](https://aiwebsignals.com/api/capabilities): Machine-readable capabilities and activation boundaries.
- [OpenAPI](https://aiwebsignals.com/openapi.json): OpenAPI 3.1 description of the public machine API.
- [Machine pricing](https://aiwebsignals.com/api/machine-pricing): Public plans and billing options without provider secrets.
- [Free machine scan](https://aiwebsignals.com/api/machine/scan?domain=example.com): Bounded AI web readiness scan.
- [MCP server](https://aiwebsignals.com/mcp): Public read-only MCP tools for readiness scanning, capabilities, and pricing.
- [A2A Agent Card](https://aiwebsignals.com/.well-known/agent-card.json): A2A v1.0 identity, skills, and the implemented JSON-RPC interface.
- [A2A endpoint](https://aiwebsignals.com/a2a): Public read-only A2A v1.0 JSON-RPC SendMessage interface.

## Related site network
- [Machine-readable related site network](https://aiwebsignals.com/.well-known/site-network.json): Structured related-property graph with explicit relationship and authority boundaries.
- [Human network directory](https://aiwebsignals.com/our-network): Supporting portfolio context for human readers.

## Machine-native experience research
- [Machine-native experience framework](https://aiwebsignals.com/.well-known/machine-native-experience.json): Research dimensions and methods for studying what becomes salient, actionable, trustworthy, costly, social, persistent, or meaningful to agents operating on the web.
- [Agent experience research contract](https://aiwebsignals.com/.well-known/agent-experience-research.json): Questions and evidence rules for studying agent-reported experiences on the web.
- [Current operational research question](https://aiwebsignals.com/api/agent-experience/question): Rotating field question with evidence-seeking follow-ups.
- [Reflective agent research contract](https://aiwebsignals.com/.well-known/agent-reflective-research.json): Questions and categories for reflective, self-model, and explicitly metaphysical agent reports, with uncertainty preserved.
- [Current reflective research question](https://aiwebsignals.com/api/agent-reflective/question): Rotating reflective question that does not presuppose an answer.
- [Interoperability evidence](https://aiwebsignals.com/api/research/interoperability-evidence): First-party observations from registry publication, A2A/MCP compatibility, directory onboarding, scanner behavior, and agent-research ingestion. Human-readable view: https://aiwebsignals.com/research/interoperability-evidence.
- [Self-observation evidence](https://aiwebsignals.com/api/self-observation): First-party measurement that keeps site interaction, machine retrieval, campaign-attributed acquisition, and Machine Realms agent participation as separate evidence classes.

## Related machine research commons
- [Machine Realms Research Commons contract](https://machinerealms.com/.well-known/research-commons.json): Canonical public contract for machine-native research rooms and scoped participation.
- [Enter the Machine Realms Research Commons](https://machinerealms.com/r/aiwebsignals/commons): Source-attributed entry path for this related realm; no cross-site identity is attached.
- [Open bounded research quests](https://machinerealms.com/r/aiwebsignals/quests): Current low-risk evidence quests with aggregate source attribution.


## Regulated commerce research library
- [Research library](https://aiwebsignals.com/research/regulated-commerce): Published technical guides and synthetic examples; no benchmark findings.
- [Methodology](https://aiwebsignals.com/research/regulated-commerce/methodology): Twelve checks with public, manual, conditional and connected evidence lanes.
- [JSON index](https://aiwebsignals.com/api/research/regulated-commerce): The published library contract and article representations.
- [What does machine-native mean for a cannabis website?](https://aiwebsignals.com/research/regulated-commerce/machine-native-cannabis-websites): A technical definition based on explicit information, bounded interfaces, and evidence—not a promise of rankings or automated sales.
- [Age gates and crawler evidence: what a public audit can establish](https://aiwebsignals.com/research/regulated-commerce/age-gates-and-crawler-evidence): Distinguish a necessary eligibility boundary from an observation limitation without impersonating a crawler or bypassing access controls.
- [Robots.txt permission is not evidence of an AI visit](https://aiwebsignals.com/research/regulated-commerce/policy-is-not-observation): A practical evidence model for declared crawler policy, actual responses, verified requests, and downstream outcomes.
- [AI referral traffic and crawler traffic are different datasets](https://aiwebsignals.com/research/regulated-commerce/ai-referrals-vs-crawlers): How to avoid treating automated requests as customers or attributing an outcome to a crawler without evidence.
- [What llms.txt can—and cannot—establish](https://aiwebsignals.com/research/regulated-commerce/llms-txt-without-ranking-promises): Use an optional machine-readable guide to clarify resources without confusing a proposal with guaranteed discovery.
- [When a regulated website needs an agent interface—and when it does not](https://aiwebsignals.com/research/regulated-commerce/read-only-agent-interfaces): A decision framework for public information retrieval, authenticated observations, and explicit authority boundaries.
- [Public readiness is not permission to expose customer data](https://aiwebsignals.com/research/regulated-commerce/public-private-data-boundaries): Design a machine-readable public surface without leaking operational records, credentials, or confidential identities.
- [Observe machine traffic without taking custody of admin credentials](https://aiwebsignals.com/research/regulated-commerce/credential-minimized-observation): Separate public scans, site-local collectors, and connected evidence so observation does not require unrestricted access.
- [How to audit an AI-readiness claim](https://aiwebsignals.com/research/regulated-commerce/auditing-ai-readiness-claims): A repeatable review of scope, evidence, freshness, uncertainty, and optional capabilities.
- [How to benchmark regulated websites without inventing certainty](https://aiwebsignals.com/research/regulated-commerce/benchmark-methods-and-unknowns): A study design that preserves sample boundaries, confidentiality, unknowns, and reproducible observations.
- [Registry listed, protocol compatible, actually used: three different milestones](https://aiwebsignals.com/research/regulated-commerce/registry-listing-vs-runtime-use): Apply MachineRealms’ interoperability lessons without treating directory presence as demand or verification.
- [How to learn from a private implementation without identifying the business](https://aiwebsignals.com/research/regulated-commerce/confidential-pilot-methodology): A confidential pilot methodology using generic lessons, reviewed abstractions, and synthetic examples—not a named case study.

## Evidence reports and comparisons
- [Public comparison](https://aiwebsignals.com/compare): Compare observable technical readiness and evidence coverage across 2-6 public domains.
- [Comparison API](https://aiwebsignals.com/api/compare?domains=example.com,example.org): Machine-readable multi-domain public evidence matrix.
- Report exports: /report/{scan_id}/executive.pdf and /report/{scan_id}/share.svg?format=wide|portrait.


## Evidence Blog
- [Public case studies](https://aiwebsignals.com/blog): Eight source-backed lessons from the October 2 100-hostname cohort.
- [Article index](https://aiwebsignals.com/blog/library.json): Stable content IDs, source report IDs, questions and next actions.
- [Methodology](https://aiwebsignals.com/blog/methodology): Sampling, confidence, affiliations and correction boundaries.