mPorts

AI discoverability

Generative Engine Optimization

Generative Engine Optimization is the practice of making a company’s verified knowledge accurate, structured and citable, so AI systems can understand and correctly represent it. The methodology follows six stages: Discover, Understand, Verify, Trust, Cite, Recommend, and it puts authority before amplification.

What changed

Buyers increasingly ask an AI system before they visit a website. The AI answers by assembling something from whatever it can find and attribute. If your company’s public knowledge is thin, inconsistent, or contradicted across sources, the answer gets assembled anyway — from whatever was available.

The failure is not invisibility. It is being described incorrectly, confidently, to someone who never sees your site.

The six stages

Authority before amplification. Publishing more content before the knowledge underneath it is correct multiplies the contradictions rather than the authority — which is why the order matters more than the volume.

  1. 01

    Discover

    Find what AI systems currently say about the company, and where it comes from.

  2. 02

    Understand

    Establish what is actually true — the entities, the products, the relationships.

  3. 03

    Verify

    Check each fact against a source that can be cited, and mark what cannot be supported.

  4. 04

    Trust

    Make the authoritative source unambiguous, structured and consistent across languages.

  5. 05

    Cite

    Give AI systems something specific and attributable to quote.

  6. 06

    Recommend

    Monitor how the company is represented, and correct the source when it drifts.

Authority before amplification

Each rung rests on the one beneath it. Publishing more content before the knowledge underneath is correct multiplies contradictions rather than authority — which is why the order matters more than the volume.

  1. Official website

    One place the company controls and stands behind.

  2. Verified company knowledge

    Each fact checked against a source that can be cited.

  3. Structured entities and relationships

    What things are, and how they relate, in a form a machine can read.

  4. Authoritative content

    One canonical answer per question the buyer actually asks.

  5. External authority

    Corroboration from sources the company does not control.

  6. Distribution

    Reach, once there is something correct to reach with.

  7. AI visibility monitoring

    Watch how the company is represented, and correct the source when it drifts.

This site is the worked example

This website is built the way the methodology describes. Every factual sentence resolves to a registered claim with a named source, an evidence tier and a review date. Each buyer question has exactly one canonical page, in both languages, and structured data may only contain facts that also appear in the visible text.

You can see the consequence on the pages themselves: there is no integration count, no percentage saved, no logo wall. Those were removed because they could not be supported, which is the methodology working rather than the methodology failing.

  • One canonical answer per buyer question, per language
  • Facts resolved from a governed registry, not written into a page
  • Structured data that cannot contain a fact the page does not show
  • A visible record of what we decline to claim

What we will not promise

We will not promise a ranking, a position in any AI system’s answers, or a visibility figure. Nobody can control what a generative system outputs, and a promise of that shape would be exactly the kind of unsupported claim this practice exists to remove.

What the work does is make the accurate answer easy to find, verify and attribute. Whether a given system uses it is not ours to guarantee.

Questions buyers ask

How is GEO different from SEO?
SEO optimizes for a ranked list of links. GEO optimizes for being understood, verified and correctly quoted by a system that assembles an answer. The overlap is real but the failure modes differ: an SEO problem is being ranked low, a GEO problem is being described wrongly.
Do we need to publish more content?
Usually not first. Publishing more before the underlying knowledge is correct and consistent tends to multiply contradictions. Verify, then publish.
Can you guarantee we appear in ChatGPT?
No, and we would be suspicious of anyone who does. What we can do is make the correct answer available, structured, and attributable.

Make the accurate answer the easiest one to find.