GEO agency: visibility where the answers are generated
More and more research ends in an AI answer rather than a result list. Whoever is not cited there does not exist in the decision process.
Generative engine optimization (GEO) makes sure a brand appears — and is represented correctly — in the answers of generative systems such as ChatGPT, Gemini, Perplexity or Amazon Rufus. The levers differ from classic SEO: entity consistency, cleanly extractable statements, structured data and verifiable sources matter more than keyword density.
How visible is your brand in AI answers?
GEO is part of the Supersmart product family from Davies Meyer. Compare answers, mentions and sources across selected AI models. We help you interpret findings and improve content and technical foundations.
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Our approach: creative engineering
We work neither as tool consultancy nor as a classic production agency, but in between: creative engineering means building the production chain behind the creative work — templates, generative modules, checkable brand rules and delivery as one system. The creative judgement stays with the team, the multiplication becomes an engineering task.
The concept stays human
Idea, visual language and tone are created as before. What follows is what gets automated: variants, formats, language versions, delivery.
Brand rules the machine can check
Colour, typography, logo usage, forbidden claims — stored as rules that apply automatically before approval instead of sitting in a PDF guideline.
Production as a chain, not a tool
Template, generative module, validation step, approval and delivery interlock. A single tool without that chain only moves the manual work elsewhere.
Impact measurable at creative level
We evaluate not only the campaign but the individual attribute: hook, composition, colour world, length. That feeds the next production round.
What we take on
Answer share measurement
We systematically test which questions mention your brand — and how accurately.
Entity & content architecture
Consistent entities plus glossary and pillar structures that models can resolve cleanly.
Structured data
Schema.org, clear fact blocks and citable phrasing instead of marketing prose.
Agentic commerce readiness
Preparing product data and interfaces so purchasing agents can process them.
How the engagement runs
- 01
Assessment
We review your processes, data situation and existing stack, and document where AI realistically creates impact today — and where it does not.
- 02
Prioritisation & business case
Use cases are ranked by effort, impact and risk. Every candidate gets a transparent calculation instead of a gut estimate.
- 03
Pilot in production
We build the first use case all the way into real operations — including system integration, approval flow and measurement points.
- 04
Scaling & enablement
What works gets rolled out and handed over: playbooks, training and governance, so you do not stay dependent on us.
Frequently asked questions
30 minutes, a concrete assessment
Describe your use case briefly — we will say plainly whether and how it pays off.