AI-driven digital marketing
Digital marketing rebuilt around how people actually search now
A growing share of commercial research never reaches a results page. Someone asks ChatGPT which SAP partners work with mid-market manufacturers, asks Perplexity to compare two vendors, or reads a Google AI Overview and stops there. In each case an answer is assembled from sources a model can retrieve, parse and trust. If your website is slow, renders its content through JavaScript, or is vague about what your company actually does, you are not in that answer.
OMAV Technology treats this as an engineering problem rather than a content problem. We make your organisation legible to machines: unambiguous entity identity, complete server-rendered HTML, structured data that matches the visible page, self-contained answers a model can lift without distortion, and measurement of what AI systems say about you today versus after the work.
Classic search has not gone away. Organic listings and paid campaigns still generate the majority of qualified enquiries for most B2B companies. The nine services below cover both: the AI visibility layer and the search and paid foundation it depends on.
The vocabulary
GEO, AEO and AIO are three different jobs
These terms get used interchangeably in marketing copy. They describe genuinely distinct work with different deliverables, different specialists and different ways of measuring success. Here is how OMAV defines and separates them.
Generative Engine Optimisation
Getting your organisation retrieved, represented accurately and cited by generative AI systems such as ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews. GEO operates at the level of the entity and the source: does the model know who you are, does it consider you authoritative on this topic, and does it name you when answering.
Answer Engine Optimisation
Structuring individual passages so that one block of text answers one question completely and correctly on its own. AEO operates at the level of the passage: question-form headings, direct answers of roughly 40 to 65 words, definition lists, comparison tables and FAQ or HowTo markup that survives text extraction intact.
AI Optimisation
The infrastructure that makes the first two possible. Crawler access policy, llms.txt and ai.txt publication, schema.org entity graphs, rendering strategy, feed and API availability, page performance, and consistency of your identity across every third-party source a model might read. AIO operates at the level of the site and the organisation.
Content Strategy
Deciding what to publish and why, before anything is written. Entity and topic mapping, question research drawn from real prompts rather than keyword volume alone, competitive share-of-answer analysis, and an editorial plan tied to commercial outcomes instead of publishing cadence.
Content Engineering
Treating published content as structured data rather than prose. Content models, reusable components, schema mapping, taxonomy and internal link architecture, and templates that emit correct markup automatically so correctness does not depend on an editor remembering.
How they fit together
One programme, five layers, in this order
Running these out of sequence wastes budget. There is no value in optimising passages on a site a crawler cannot render, and no value in publishing volume before you know which questions matter.
AIO: make the site readable
Server-rendered HTML, crawler permissions, schema graph, llms.txt, Core Web Vitals. Without this the rest is invisible.
Strategy: decide what to say
Entity map, priority questions, share-of-answer baseline, editorial plan. Establishes what is worth writing before anything is written.
Engineering: build the containers
Content models, templates, components and schema mappings so every new page is correct by construction rather than by review.
AEO: write extractable answers
Question headings, direct answers, key-fact tables, FAQ and HowTo markup. This is where individual passages become quotable.
GEO: build source authority
Entity consistency across third-party sources, digital PR, factual accuracy monitoring, and tracking which prompts return you by name.
The foundation layer
SEO, PPC, social and digital PR still do most of the work
AI visibility compounds on top of conventional search performance rather than replacing it. Models disproportionately retrieve pages that already rank, and sites with strong technical foundations get crawled more often and more deeply.
Search Engine Optimisation
Crawlability, indexation, information architecture, Core Web Vitals, on-page optimisation and content quality. The same technical work that helps Googlebot helps GPTBot, because both need to fetch and parse your pages efficiently.
PPC Management
Search and paid social campaigns with proper conversion tracking, negative keyword hygiene, landing page alignment and honest budget reporting. Paid remains the fastest route to test which messages and offers actually convert.
Social Media Marketing
Channel planning, content systems and community activity for B2B audiences. Social profiles are also a signal source: models read LinkedIn company pages and consistent descriptions there reinforce entity resolution.
Digital PR and Link Acquisition
Earning mentions and links from sources that are themselves trusted and frequently retrieved. For GEO specifically, being described accurately on a well-regarded third-party site matters more than raw link count.
Deliverables
What you actually receive
Every engagement produces artefacts you keep and can hand to another supplier. Nothing is locked in a proprietary dashboard.
AI visibility baseline
A documented record of how ChatGPT, Claude, Gemini, Perplexity and AI Overviews currently answer your priority questions, which competitors appear, and which facts they get wrong.
Technical remediation plan
A prioritised, developer-ready list of what to change, with the reason, the expected effect and the effort estimate for each item.
Entity and schema specification
The canonical description of your organisation, its services and identifiers, plus the schema.org graph that expresses it, ready to implement or already implemented.
Content models and templates
Reusable page structures that emit correct markup automatically, so future content stays compliant without ongoing specialist review.
Editorial plan
Priority questions mapped to pages, with owners, formats and the commercial reason each one exists.
Measurement framework
Prompt tracking, citation monitoring, organic and referral traffic from AI surfaces, and the reporting cadence agreed at kick-off.
What we will not claim
Where the honest limits are
No agency controls what a language model outputs. Model providers change retrieval behaviour, weighting and citation formatting without notice, and results vary between sessions for the same prompt. Anyone guaranteeing a position in AI answers is describing something they cannot deliver.
What can be controlled is whether your information is available, accurate, unambiguous, well-structured and published by a source the model has reason to trust. That materially changes the probability of being retrieved and cited, and it is measurable. It is not a guarantee, and we will not describe it as one.
Timelines are similarly honest. Technical and structural work shows up in crawler behaviour within weeks. Changes in how models describe an organisation typically take longer, because training data, retrieval indexes and third-party sources all update on their own schedules.