What is aeo®?

aeo® — ainomics engine optimization — is the method developed by ainomics® to make companies visible in generative AI systems and to actively steer how they are represented. aeo® combines systematic AI search monitoring with strategic communications analysis: not just being found, but being correctly understood and accurately cited. aeo® creates visible results.

What sets it apart is the claim: aeo® treats AI visibility not as a technical project but as a management task — with defined targets, KPIs, responsibilities and a fixed rhythm. What is measured can be steered; what is steered improves.

Why a dedicated method is needed

Three observations stand at the beginning of aeo®. First: search becomes a question — people no longer skim search results, they trust AI answers directly. Second: awareness is not visibility — a company can be known to AI systems and still be described incorrectly, positioned wrongly or distorted. Third: communication without control — without monitoring, the AI alone decides what image of the company emerges.

Isolated measures fall short here. Those who only optimize content are found, but not properly understood. Those who only communicate build awareness without measurability. And those who only measure change nothing. aeo® therefore connects all three levels in one loop, in which every measure feeds a KPI.

The aeo® model: three disciplines, one loop

aeo® unites three disciplines that otherwise work apart. Generative Engine Optimization delivers technical and content findability: structured data, citable content, clean page architecture. Digital consulting anchors the topic in corporate steering: targets, priorities, KPIs, responsibilities. Communications & PR build on what convinces AI systems most: independent third-party sources, consistent messages, trust.

The interplay is what matters: consulting defines what should be achieved. GEO makes the company’s own channels machine-readable for that purpose. Communications ensures that third parties tell the same story. And monitoring shows whether AI systems have adopted it. GEO optimizes for being found — aeo® optimizes for being understood.

The aeo® loop in four steps

• Understand: who asks what, in which role, with which goal? Persona mapping and the prompt set define the measurement basis. Implemented in phase 1: Audit & Diagnosis.
• Measure: how visible is the company today — per AI system, per topic field, against the competition? The baseline makes progress provable.
• Implement: build content, structured data and third-party sources along the roadmap — owned and earned content, in clear stages. Implemented in phases 2 and 3.
• Steer: measure, report and refine monthly — and react to changes in the AI systems. Implemented in phase 4, the aeo® loop. Then the cycle starts again.

Persona mapping: who asks decides

AI users do not search, they ask — in natural language, with a role, context and goal. A managing director asks differently from a buyer, a patient differently from a referring physician. Persona mapping describes these askers precisely: their role, their prior knowledge, their decision situation and the language in which they ask. Only once you know who is asking can you identify the right prompts — before they are even asked.

Prompt sets: the measurement basis of the method

From persona mapping comes the prompt set — a structured collection of real user questions that every measurement is aligned to. It comprises three groups:

• Branded prompts are direct queries about the company — for example about services, assessments or comparisons of the brand. They test whether AI systems describe the company correctly.
• Non-branded prompts are generic subject-matter questions without naming the brand — for example asking for the right provider or the right method. They measure the hardest form of visibility: appearing in answers without having been named.
• Customer journey prompts map the questions along the decision journey — from initial orientation through comparison to the final choice. They show at which stage visibility is missing.

Funnel mapping: every stage needs its own answers

People ask different questions on the way to a decision — and AI systems select different sources depending on the type of question. Funnel mapping therefore assigns prompts to the decision stages: at the beginning, definitions and orientation knowledge matter; in the middle, comparisons and criteria; at the end, concrete provider information and trust signals. Every funnel stage gets its own prompts, its own content — and its own KPI.

The five stages of AI visibility

AI visibility does not emerge all at once, but stage by stage. aeo® distinguishes five stages — each one building on the previous:

• Stage 1 — Mention: the company appears in AI answers at all.
• Stage 2 — Correct description: the representation is accurate — services, positioning and facts are rendered correctly.
• Stage 3 — Recommendation: the company is actively recommended for relevant questions, even without being named in the question.
• Stage 4 — Link: AI systems link to the company’s own pages — visibility becomes measurable traffic.
• Stage 5 — Cited source: the company is regularly the source AI systems draw on for answers in the topic field — the strongest position in the market.

KPIs and dashboard

Each of the five stages becomes measurable through a clearly defined KPI — from mention rate to citation rate, reported per AI system and prompt group. Measurement is always against two references: the company’s own baseline from the audit and the competition. This creates a dashboard that makes progress visible, justifies priorities and enables reporting at management level.

Monitoring across several layers

No single tool captures AI visibility completely. aeo® therefore combines several observation layers: the technical layer, showing which sources a system selects and whether the company is cited; the brand layer, showing how the company is described and in what tone; and the competitive layer, tracking the company’s own market position over time. Because there are no generally accepted industry benchmarks for citation rates, aeo® works with its own comparison values — built from the baseline and updated with every report.

Owned and earned: the two levers

Owned content is the direct lever: your own website, knowledge hub, FAQ, whitepapers, press section — full control, quick to implement. Earned content is the indirect, stronger lever: from an AI system’s perspective, authority arises from the repeated association of a source with a topic — through studies, expert articles, media coverage and independent voices, bundled across several channels at once. aeo® plays both levers in a coordinated way: the company’s own channels provide the citable substance, third-party sources provide the confirmation.