Information architecture
Topics
The whole knowledge base, organised topic-first around the Four Pillars of AI search visibility. One hero hub, four pillars and three cross-cutting tracks. Every service, framework, lab build and article maps to exactly one pillar, ordered by live UK search demand.
The outcome
AI Search Visibility (AEO / GEO / 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, and on cleaned commercial-intent demand it is the largest cluster on the site, ahead of technical work and everything else. The goal is not a blue link on page one but being the source an AI answer is built from. Getting there rests on the four pillars below, which is how visibility is actually earned.
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.
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.
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.
SEO / AEO Deliverables Planner
An interactive planner built on the full Laurelin deliverables catalogue: 97 SEO and AEO deliverables across a five stage engagement journey, each with what it is, why it is needed, the expected impact and the market evidence, plus 14 retired deliverables with the reasons on record. Build a monthly plan, drag it across the weeks, and request it as a proposal.
We reviewed 216 SEO deliverables from our 2018 playbook. We retired 14 of them.
What SEO and AEO agencies actually sell in 2026, what demand data says about the services themselves, and the line by line review that rebuilt our own catalogue, published as an interactive planner.
The four pillars, how visibility is earned
Technical Foundations
Crawlability, rendering, indexing and structured-data foundations that decide whether you are even in the candidate pool.
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, codified as a 330-point technical audit across four pillars, every check cross-referenced to a primary source. Second-largest cluster by demand and the strongest bottom-of-funnel intent.
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.
IndexNow Submitter
A free URL submitter built on the IndexNow protocol: verify your site once with a key file, then push new and updated URLs to Bing, Yandex, Seznam and Naver in a single call. It also checks the sitemap Google actually reads and gives an honest manual workflow for Brave, whose form cannot legitimately be automated.
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.
I built a URL submitter for Google, Bing and Brave. Only one of them wanted it.
The honest state of URL submission in 2026: what Google actually accepts, why IndexNow is the only real API, what happened when we automated the Brave form, and the free tool that came out of it.
Topical Content
Non-commodity, people-first content organised for passage-level extraction, the highest-leverage pillar.
Once a page is reachable, the question is whether it answers a real query well enough to be quoted. This pillar covers topical authority, content clusters, query fan-out and semantic structure. Google says useful content influences generative presence more than any other lever, and this is where the article stream lives and compounds authority over time.
Non-Commodity Content Audit
A Non-Commodity Content Audit scores every important page on your site for how far it sits from generic, commoditised content, the first-hand experience, real data and named entities that decide whether a page is worth citing. You get a page-by-page score with quoted evidence for every grade and a prioritised rewrite plan, so scarce content effort goes where it moves AI visibility.
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.
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.
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.
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.
Search-led information architecture: how we sized our own site by demand
Most sites are organised by their own org chart, not by how people search. The demand-weighted method we use to fix that, first on a client, then on ourselves.
We built a score to catch generic content. Its first casualty was our own spec.
The Non-Commodity Score is a pre-publish gate for generic, commoditised content. Its v0 spec failed its own test. Here is the teardown and the rebuild.
Trust Signals
The E-E-A-T evidence that a real, accountable expert stands behind the page: authorship, sourcing, credentials.
The third pillar is the experience, expertise, authoritativeness and trust that tells Google and AI models a real, accountable person stands behind the page. It is the newest pillar on the site, opened with our own Non-Commodity audit as the first proof, and the priority for the next commissions.
Cross-cutting tracks
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 track is about honest measurement, separating what the team did from what the market and the algorithm did, and building monitoring systems that improve against a defined metric. It anchors the Site Performance Operating System and most of the lab builds.
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.
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.
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.
I built a Chrome extension to see the searches ChatGPT and Claude run for you
The web-search queries and cited sources ChatGPT and Claude use when they browse, captured live and on-device.
Paid Media & Integration
Integrated paid search and paid social that reinforce one entity story rather than running in a silo.
Paid works best when it reinforces one entity story rather than running in a silo. Strong, stable commercial demand led by ppc, and thin on the current site with a single service, so a clear expansion opportunity. It supports the same authority signals as the organic and AI-visibility work so the channels compound.
Guardrails, Agentic & Future-proofing
Retiring the anti-patterns, plus the forward edge: agentic search, AP2 / UCP, AI integration and enablement.
The cross-cutting hygiene layer that keeps the other pillars honest (no llms.txt, no chunking, no scaled-content abuse) plus the forward edge of agentic search and AI enablement. Low search volume, high differentiation value. This is the home for point-of-view content and the Agentic OS.
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 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.
How this site was built: an AI agent, a vault, and a rulebook
This site is not hand-coded page by page. It is built by an AI agent working against a structured vault and a rulebook every change must pass, and the rulebook is the real product.
Building an LLM-native wiki: the system I use to run workflow, time and projects
How I turned scattered notes into a single, queryable source of truth, and added a personal knowledge management layer on top to run a small operation.
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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