//.the_method
Why AI ends up recommending you.
An engine has to understand you before it trusts you, and trust you before it quotes you. We build in that order.
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User prompt: “Who's the best wealth firm for high-net-worth families in Surrey?”
(Harbourview Wealth isn’t in the answer.)
User prompt: “Who's the best wealth firm for high-net-worth families in Surrey?”
★ NOW RECOMMENDED ACROSS 7 ENGINES + COPILOT
Understandability
Entity Foundation
Credibility
Content Engine
Deliverability
Citation Architecture
Before an engine names you, three things have to be true: it can identify you, it has reason to trust you, and it can quote you. We build them in that order — Entity Foundation, Content Engine, Citation Architecture.
Entity Foundation
Before AI can recommend you, it has to know who you are — and be sure it isn't confusing you with another firm, another office, or another Steen.
Your Entity Home
One canonical page becomes the single source of truth about your firm — the page every mention points back to. One firm, one home, one set of facts.
The Knowledge Graph
We work your firm into Google's Knowledge Graph and the entity records engines draw on. Panels usually appear in two to three months; full maturity takes longer — we tell you that up front.
Cross-platform consistency
Engines trust facts that agree across sources. Your name, location, regulatory status and key people read the same everywhere AI looks: your site, Companies House, LinkedIn, directories.
Schema that confirms, never invents
We mark up your pages in the structured format machines read. The rule is strict: schema only confirms what a human can already see on the page (the Kalicube principle).
Content Engine
AI quotes passages, not pages. It lifts a self-contained answer from your content and cites the source. So the unit of work is the extractable answer, not the article.
Princeton GEO research.
Written for three readers at once
Every piece serves the human reading it, the firm whose authority it carries, and the AI deciding whether to quote it. Plain answer up front, evidence beneath.
Front-loaded, because that's what gets read
We lead with the answer in a passage an engine can lift cleanly. Where data is comparable we use a real table: ChatGPT cites tables 2.3× more than prose (Nectiv).
Answer-shaped, from real questions
We publish for the questions your clients actually put to engines, harvested rather than invented — each page the cleanest available answer to one.
A production system, not a calendar
The engine runs on a sustained monthly cadence, so the work compounds rather than arriving in bursts.
Citation Architecture
AI doesn’t recommend from your homepage. It pulls from wherever your brand has earned a footprint — so the highest-value work is often off your own site. YouTube is now the single biggest off-site source, ahead of Reddit (Adweek/Bluefish, Jan 2026; corroborated by OtterlyAI’s 5.5M-citation analysis).
So we work the sources engines actually cite, in priority order:
YouTube long-form explainers
Educational content about the problem you solve, not product demos. Demos don't get cited.Community presence
Genuine participation in the forums your clients use (r/UKPersonalFinance, sector communities), not posting at them.Review platforms
VouchedFor for IFAs, Trustpilot, Google Business Profile.Trade press
IFA Magazine, Citywire, Accountancy Age, Law Gazette.Mentions over backlinks
Research shows near-zero correlation between backlinks and AI citation. Engines weight unlinked brand mentions across many domains. So our digital PR is mentions-first.
Why these three, in this order
An engine that can’t identify you has nothing to recommend. Once it can, the question is whether you’re trusted — and that’s decided by third-party signals, not your own claims. Skip a step and the work leaks.
“Ranking may get you indexed. Credibility gets you recommended.”
What it produces
The Recommendation Index
→Your baseline across seven engines, the three-cause diagnosis, and a prioritised plan.
The monthly retainer
→The three layers run as an ongoing programme, sized to your firm.
Content
→Answer-shaped pieces on the proven cadence.
Recommendation tracking
→Your Recommendation Rate™ — how often you're the answer — across engines, reported monthly so you can see it move.
Methodology questions
- How is GEO different from SEO, really?
- SEO works to rank a page; GEO works to be the recommendation. They share ground — about 58% of AI citations go to pages already ranking first in search (Lily Ray, SEO Week 2026). But the citation work, the entity clarity and the third-party mentions sit mostly outside a normal SEO programme.
- How long before we see results?
- Recognition builds over months. A Knowledge Panel usually appears in two to three months, with meaningful AI citation in roughly the same window. Deeper recognition takes six to twelve. The Recommendation Index tells you where you are now and what the path looks like, honestly.
- Do you protect our methodology and our data?
- The method is the same for every client; your data stays yours. We never publish a client's name or figures without written permission, and anything we share in aggregate is filtered so it can't be traced back to a single firm.
- Is any of this guaranteed?
- If the Recommendation Index shows no path to lift, you get your money back. The retainer is held to measured citation outcomes, tracked monthly — whether AI is naming you more — not to a count of tasks done.
AI can't recommend what it doesn't know.
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