AI visibility
AI visibility is not magic
The phrase “we influence AI search results” sounds attractive, but it is usually the wrong promise.
A more honest starting point is this: a business can make its website easier for AI systems and agents to read,
compare, and cite by adding a minimal machine-readable layer.
Short version: Agntbase does not promise ChatGPT rankings, guaranteed recommendations, or instant leads.
We build a structured representation layer around a business so machines have fewer reasons to guess.
GEO, AEO, SEO and AI visibility are not the same thing
SEO is mostly about helping search engines crawl, understand, and rank pages. GEO and AEO are often used for
“generative engine” and “answer engine” optimization, but the market is still full of vague promises.
AI visibility is narrower and more testable: how clearly can an AI system understand the business, its services,
its location, its trust signals, and the best route for action?
That does not mean a file alone changes model behavior. It means the site becomes less ambiguous.
Instead of forcing an agent to scrape scattered HTML, the business gives it a cleaner source of truth.
What the machine-readable layer includes
Profile
Company JSON profile
Structured identity, services, contacts, locations, trust fields, and canonical links.
Entry point
llms.txt
A short human- and AI-readable description of what the business does and where to find canonical data.
Manifest
agenthub.json
A compact entrypoint that links the site to the canonical profile and agent-facing resources.
Search layer
JSON-LD snippet
Baseline structured data for search engines and other machine readers that understand linked data.
What this can and cannot do
- It can make the business easier to parse, compare, and route inside agent workflows.
- It can reduce ambiguity around name, services, location, contacts, and official source of truth.
- It can create a measurable before/after readiness score for the site.
- It cannot guarantee placement in ChatGPT, Perplexity, Google AI Overviews, or any recommendation surface.
- It cannot fix a website that has no real information, no trust signals, and no clear offer.
The practical workflow
- Run a free AI readiness check and identify missing machine signals.
- Build a canonical profile from confirmed public facts and owner-approved fields.
- Publish the canonical profile in the hub as a stable source of truth.
- Generate the small website package:
company-profile.json, llms.txt, agenthub.json, and JSON-LD.
- Place the files on the company website and re-check the score.
This order matters. The hub keeps the full canonical profile; the client site receives only the minimal layer it needs.