For SMEs
For many customers, the first contact with your business is no longer Google: it's an answer generated by an AI. And appearing in that answer can't be bought with an advertising budget — it's built in the data infrastructure.
How AI engines understand and cite your business
Answer engines don't crawl pages the way Google does: they compose responses in real time by extracting relationships from verifiable sources. If the company's information is absent, ambiguous, or machine-unreadable, for the AI that company simply doesn't exist.
1. GEO (Generative Engine Optimization)
Ensuring your brand, services, and products appear with priority in complex purchase searches — where the customer isn't looking for a keyword but for a solution.
2. Brand Integrity and anti-hallucination
Anchoring company data, locations, and pricing to stable sources (Schema.org, Wikidata) so that models don't invent incorrect details. Your reputation stops depending on what the AI imagines.
3. Enterprise RAG Readiness
Documentation, catalogues, and technical files structured into a proprietary knowledge graph, ready to be queried by an internal AI assistant with precision — not probability.
Business AI Readiness
Preparation of corporate, commercial, and catalogue information to be fully accessible and indexable by generative engines and intelligent agents.
Areas of interventionSmall and medium-sized businesses that want to position their brand and offering where customers ask complex questions.
Why classic SEO is no longer enough
SEO was built for a world of lists: you optimise to rise in the results list, and the user chooses. Answer engines skip the list and compose the response directly, often without the user visiting any site. Ranking first on Google no longer guarantees appearing in that synthesis.
The stakes have changed. Having the right keywords isn't enough: you need structured data that explicitly declares what the company does, where it operates, and which verifiable entities it's connected to. The difference between a page a search engine must interpret and one it can read without any margin of error.
When a business is ready for AI search, and when it isn't
Some signals show the work is needed now: AI systems describe the company vaguely or incorrectly, confuse it with another, or don't mention it at all when people search for solutions in its sector. If a potential customer asks ChatGPT who offers what you offer and your name doesn't appear, that customer doesn't even know they excluded you.
It's not always the right moment. If the company doesn't yet have a clear offer or stable information to publish, positioning comes first and then structuring. Structuring confused data only makes it more readable, not more convincing.
The other half of the work: your internal data
External visibility is only half the value. The same structured knowledge that makes the company readable to public engines can power an internal assistant: a system that answers customers or colleagues by drawing from verified catalogues, manuals, and procedures instead of inventing.
This is the RAG principle applied to the enterprise. The more internal data is organised and linked, the more precise the answers and the fewer hallucinations. The semantic infrastructure built to be found also becomes the backbone of company knowledge.
The AI Readiness journey for businesses
The value isn't in the final code, but in the decisions that precede it. You start from what to communicate and arrive at the syntax, never the reverse.
Phase 1: What you want to be known for
AI Exposure Audit & strategyBefore the code comes a decision. I map how AI systems interpret the company today and define with you what anyone searching should know — and why. Positioning is chosen here, not chased afterwards.
Phase 2: The information basket
Data retrieval and verificationThis is the real work. I retrieve and verify the information that supports every claim: company data, products, locations, certifications, case studies, numbers. I decide with you what goes in, what stays out, what is genuinely demonstrable. A company isn't declared — it's documented.
Phase 3: Weaving the entities
Knowledge graph & entity linkingNo company exists in isolation. I connect the entity to those already verified that it's linked to: partners, suppliers, sector, territory, recognitions. The company's meaning emerges from relationships, and that's what an AI system reads to situate it.
Phase 4: Implementation and stewardship
JSON-LD, Schema.org, RAG, monitoringOnly at this point do I translate decisions into JSON-LD and structured data, and keep the infrastructure alive over time. The syntax is the last step, not the work: if a plugin could generate it, it would mean the upstream decisions hadn't been made.