AI for Marketing and Growth
Growth work where the measurement is as serious as the tactics. Eleven engagements across AI search visibility, content, personalisation, media and attribution — each reported on what it can prove and honest about what it cannot.
Marketing is where AI claims go unchecked.
This is the discipline with the least verifiable supplier claims in the industry. Nobody outside the model providers can see how AI answers choose their sources, platform-reported conversions add up to more orders than you took, a large share of declared conversion wins do not replicate, and personalisation is usually assumed to work rather than measured against a holdout. The confident advice is abundant; the evidence is thin.
So the method here is unglamorous. Baseline before changing anything, hold back a control where one is possible, run experiments that are powered before they are run, and report the movement we cannot explain alongside the movement we can. Where a result came from a model update or a seasonal effect rather than our work, we say so — which occasionally means reporting a good quarter we did not cause.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
Generative Engine Optimisation (GEO and AEO)
Content built to be quoted, entity clarity and corroboration — with honest reporting on what cannot be measured.
Read more →AI Visibility and Brand Monitoring in LLMs
Tracked prompts across surfaces, share of mention against competitors, and correction of inaccurate claims.
Read more →AI SEO Services
AI applied where it helps — clustering, gap analysis, internal linking, log analysis — not to mass-produce pages.
Read more →Programmatic SEO at Scale
Templated pages over a real dataset, with quality gates and index management — thousands, not hundreds of thousands.
Read more →AI Content Production Systems
Source-grounded drafting, brand voice from your own archive, and review gates that cannot be skipped.
Read more →Personalisation Engine Implementation
Real-time decisioning proven by holdout experiment, with cold start, consent and creepiness handled deliberately.
Read more →AI Ad Creative and Media Buying
Variant production with rights and disclosure handled, and control of what automated bidding leaves you.
Read more →Marketing Analytics and Attribution AI
Incrementality experiments and mix modelling, with honest treatment of what attribution cannot resolve.
Read more →AI Lead Scoring and Enrichment
Scoring trained on real outcomes, enrichment coverage measured by segment, and a score sales will act on.
Read more →Conversion Rate Optimisation with AI
AI to find what to test and read behaviour at scale; proper experiment discipline to decide what worked.
Read more →Social Listening and Sentiment Monitoring
Aspect-level sentiment tuned to your domain, with alerting that tells a spike from a crisis and routes to an owner.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
Be found in AI answers
GEO and AEO built on the mechanisms that plausibly drive citation, with AI visibility monitoring to tell you whether it worked and whether what is said about you is even true.
Earn search traffic at scale
AI SEO applied to analysis rather than page production, programmatic SEO sized to real demand, and content systems where experts stay in the loop.
Convert the traffic you have
Personalisation proven against a permanent holdout, conversion optimisation with proper experimental discipline, and lead scoring sales will actually work from.
Know what worked
Attribution built on incrementality rather than last click, creative and media for platforms that bid for you, and social listening that routes to an owner rather than a dashboard.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
Is GEO different from SEO?
Less than the marketing around it suggests. The technical and authority foundations are identical, and a site that fails at SEO will fail at AI answer surfaces too. What genuinely differs is that the winning unit is a quotable passage rather than a ranking page, that being described consistently on third-party sources matters more, and that the outcome is often a citation without a click. See GEO and AEO.
Can you guarantee results?
No, and we will not pretend otherwise. Nobody outside a search engine or model provider controls its behaviour, and both change without notice. What we commit to is a baseline before we start, controls where they are possible, experiments powered before they are run, and reporting that separates what we caused from what happened anyway.
Should we use AI to produce content at volume?
Only with genuine expertise behind each piece and a review gate that cannot be skipped. Mass-produced content with nothing distinctive to say performs poorly and, at scale, affects how the whole domain is judged. Content systems are built to make experts faster, which is the version that works.
Why do our platform-reported conversions not match finance?
Because every platform uses its own attribution and counts conversions it claims credit for, with no visibility of the others — so summing them double-counts, often substantially. The fix is incrementality experiments to anchor the truth and a mix model reconciled to actual revenue. See marketing analytics and attribution.
What are the governance implications of AI in marketing?
Real ones. Generated creative raises rights and disclosure questions, personalisation raises consent and fairness questions, and customer-facing AI content falls under the EU AI Act's transparency duties. These are settled before production rather than at legal review — see AI governance, security and compliance.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.