Insights
Topic clusters
Frameworks, services and lab builds, organised by the topics we go deep on. Browse a cluster to see how the pieces connect.
AI Search Visibility (AEO / LLMO)
Being the cited, trusted answer inside AI Overviews, ChatGPT, Perplexity and Gemini, not just ranking a blue link.
This is the centre of gravity. The goal is not a blue link on page one but being the source an AI answer is built from, the cited, trusted reference inside AI Overviews, ChatGPT, Perplexity and Gemini. Getting there rests on clear entities, retrievable answers and demonstrable authority, which is what the frameworks and services in this cluster are built to deliver.
AI Search Visibility (AEO / LLMO)
AI Search Visibility work makes your brand and its leadership the cited, trusted source when buyers, investors and journalists ask an AI about your market. SEO competes on keywords; AEO competes on entities, and most organisations are currently invisible or ambiguous to the models.
AEO + LLMO Strategy
A twelve-month AEO and AI-visibility roadmap built around priority topic clusters, keyword targets, content cadence, link priorities and AI-readiness actions, mapped to monthly milestones and your commercial goals.
Content & Newsroom Strategy
A content strategy and production pipeline that turns raw material into SEO/AEO-optimised articles with structured data, author schema, internal links and People-Also-Ask coverage, designed so authority compounds to named authors, not just pages.
Instant AEO
An end-to-end programme that runs technical audit, opportunity analysis, competitor benchmark, content strategy, backlink plan and a 12-month roadmap in sequence, then adds a new site build with optimised information architecture and full deployment.
The Four Pillars Framework
The Four Pillars Framework groups the working levers of AI search visibility into four pillars, Technical Foundations, Topical Content, Trust Signals and Authority Network, plus a cross-cutting guardrails layer. It is synthesised from Google’s own primary sources on how generative AI features select what to cite.
Query Fan-Out
Query fan-out is the pattern where a single user question is silently decomposed into many concurrent sub-searches that run in parallel; the system then assembles the evidence into one synthesised answer. It means a page no longer needs to rank for one head keyword, it needs to be the best-cited page across the cluster of fan-out queries around its intent.
AI Search Query Capture
A free Chrome extension that reveals the web-search queries ChatGPT and Claude run when they browse, and the source URLs they cite, captured live and on-device. It makes query fan-out visible, the bridge between a natural-language prompt and what actually gets retrieved and cited. Live on the Chrome Web Store.
EventCapture
Event content, keynotes, panels, podcasts, notes, photos, evaporates after the event. EventCapture records it on a phone, offline-first, and turns it into attributed, AEO-optimised content the same day: event reports, articles, social posts, show notes and a quote bank.
Seasonal & Trend Validation
A content-pipeline feature that validates a search term against seasonal peak data and trend direction before use, showing the peak month, auto-inserting the highest-volume term into the title and heading, and alerting if the chosen term is currently off-peak.
Entity Authority
Making a brand and its leaders disambiguated, verifiable entities in Google’s knowledge graph.
Before a machine can cite you, it has to know who you are and trust it has the right entity. This cluster is about becoming a disambiguated, verifiable entity in the Google Knowledge Graph and across the wider web, through structured data, consistent sameAs references and a coherent authorship signal.
Entity Authority (Pillar 4)
Entity Authority is how clearly Google can identify who an author or organisation is, and how consistently that identity is corroborated across the surfaces it trusts. It is not authority itself, it is authority’s resolvability. The work is to make real, earned authority legible to the knowledge graph, never to manufacture it.
AEO Schema Scanner
A free Chrome extension that reads any page’s structured data and shows three things: what is detected, what Google has deprecated or demoted, and what is missing to be the trusted answer in AI search. It also flags incomplete Organization and Person entities (missing sameAs, @id, logo, knowsAbout, jobTitle). The scan is 100% on-device. Open source.
Knowledge Graph Gap Analysis
A method for finding what is missing in a knowledge base by visualising it as a graph and identifying structural holes, dense clusters of related ideas that are not connected to each other. Bridging a structural hole tends to produce original, non-generic insight, because the territory between two developed clusters is by definition under-explored.
Technical AEO
The crawlability, rendering, indexing and structured-data foundations that decide whether you are even in the candidate pool, codified as a 330-point audit across four pillars.
None of the visibility work matters if a machine cannot crawl, render and index the page in the first place. This is the foundation layer, and I have codified it as a 330-point technical audit across four pillars, every check cross-referenced to a primary source. Browse the audit to see exactly what I check and how to find each issue on your own site.
Technical AEO Audit
A 330-point technical and AI-readiness audit. Every check is cross-referenced to a Google primary source and clustered into the Four Pillars of generative search, so you see not only what is wrong but why it matters and how to fix it. It surfaces the crawl, indexing, content, trust and entity issues that quietly suppress rankings and AI citations, with a fix list prioritised by competitive impact.
Google AI Search Optimisation
Google’s official guidance is blunt: AI features pull from the same standard search index, so there are no AI-specific shortcuts, file formats or markup. Optimising for AI Overviews and AI Mode is optimising for human value, clarity and SEO fundamentals, and explicitly not the fads being sold around it.
AEO Graph View
An Obsidian-style visual node graph of a website where each node is a page and each edge an internal link, with toggleable data layers, rankings, backlinks, status codes, on-page scores, analytics, technical issues and proposed information architecture, so structure and performance are visible at a glance.
Internal Linking Intelligence
A feature that auto-generates internal link suggestions from an uploaded site crawl, producing contextually relevant links from real structure rather than generic keyword matches, and flagging the missed opportunity when no crawl is provided.
Measurement & Attribution
Joining what the team does to what the site does, attribution that survives a CFO conversation.
Search and AI visibility only earns budget when it can be tied to outcomes a CFO will accept. This cluster is about honest measurement, separating what the team did from what the market and the algorithm did, and building systems that improve against a defined metric rather than a vanity number.
Competitor & Opportunity Analysis
Two connected analyses: an opportunity gap that sizes the organic prize available to you in revenue terms, and a competitor benchmark across organic, paid and social that shows exactly where you are losing ground and what to fix first.
Site Performance OS
A four-layer operating system that captures every task (input), measures every metric (output) and joins them through an attribution model that survives a CFO conversation, closing the gap that gets agencies fired when the algorithm gives or takes.
The Site Performance Operating System
A four-layer methodology for linking marketing activity to measurable site performance: capture the inputs (every task), measure the outputs (every metric), join them through attribution windows, and surface all three on a cadence, so value is provable rather than asserted.
The Agentic OS
The Agentic OS turns an LLM assistant from a passive question-answerer into an active operating system by codifying work into a four-layer hierarchy: Domains, Tasks, Skills and Automations. Codifying behaviours into repeatable Skills, rather than running them ad hoc, makes the model a reliable team member rather than a slot machine.
The Auto-Research Loop
An autonomous optimisation loop where an AI agent iterates on a single target, a prompt, module or Skill, to maximise a scalar metric computed by an automated evaluator. Hypothesise, modify, evaluate, keep if better else reset, repeat. The binding constraint is not the loop; it is knowing what to measure.
Domain Gap Alert
A lightweight hook: enter a seed term, the tool pulls related search terms, checks whether your domain ranks for any, and if it does not, generates an alert, "you are invisible for these terms, here is the cost of that." It quantifies the gap rather than asking a prospect to trust that search matters.
Paid Media
Integrated paid search and paid social that reinforce one entity story rather than running in a silo.
Paid search and paid social work best when they reinforce one entity story rather than running in a silo. This cluster covers integrated paid media that supports the same authority signals as the organic and AI-visibility work, so the channels compound instead of competing.
PPC Audit & Strategy
A paid-search audit that surfaces wasted spend, negative-keyword gaps, quality-score and structural issues, paired with a strategy that sets budget allocation, campaign structure, audience targeting and a 90-day launch plan.
AI Integration & Training
A workflow-by-workflow map of where AI saves your team time, ranked by effort versus impact, with recommended tools, an implementation roadmap and a training plan so the capability sticks after the engagement ends.
The field notes
Frameworks, builds and what is changing in AI search.
Sent occasionally, never noise. The thinking behind the work, and the experiments before they ship.
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