From website pages to a managed business entity for AI agents.
Agntbase turns scattered business information into a canonical, verifiable and machine-readable layer: profile, registry, permissions, analytics and the foundation for future agent-to-service interaction.
This is not AI SEO and not a paid directory. It is infrastructure for reducing ambiguity when AI systems research, compare and route businesses.
The layers behind AI-readable business representation.
A normal website explains a company to people. The agentic layer gives AI systems a structured way to understand identity, trust, limits, routes and possible next actions.
Website
Human-facing pages, UX, content, SEO, forms, cases and brand.
Canonical profile
company-profile.json, llms.txt, JSON-LD, canonical hub profile, and optional A2A Agent Card when a real agent interface exists.
Registry
Normalized business entities with public profile routes and JSON endpoints.
Trust
Source map, provenance, verified fields, owner confirmation and review status.
Freshness
Last checked, stale fields, review due dates and confidence by section.
Ownership
Claim profile, permission to edit, verification methods and conflict handling.
Analytics
Human events, AI bot reads, entrypoint access and payment/report signals.
Reputation
Licenses, official pages, contacts, publications, cases and public proof.
Intent
When a company is a fit, when it is not, and what scenario it serves.
Protocol
Rules for agents: allowed reads, disallowed actions, preferred handoff.
Actions
Lead, quote, consultation, document request and payment handoff schemas.
Feedback loop
What was read, what was missing, and why a company won or lost a comparison.
What exists now, what is partial, what comes next.
The current product is intentionally simple: site check, canonical profile, client files, registry, analytics and paid package flow. The deeper action and feedback layers are the next product frontier.
Live now
Partial
Next layers
No magic. Lower ambiguity.
Agntbase does not promise rankings or guaranteed AI recommendations. The practical promise is narrower and stronger: publish clearer official facts, machine-readable files, trust context, permissions and measurable entrypoints.
Check the site first. Build the profile second.
The fast path is not rebuilding the website. It is understanding what AI can read now, then adding the canonical profile and machine-readable package where it creates the most clarity.