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Three days of LLM SEO, in one hour.

Case studyEnd-to-end process automationVideo 3:10

Keyword research, query generation and a full semantic audit. Three of the hardest LLM optimisation tasks, chained end to end, with each phase’s output becoming the next phase’s input. What used to take a senior team three days now runs hands-off in under an hour.

Category
Workflows
Time to value
8 weeks, build and tune
Stack
Claude, Google Sheets, search APIs, crawl tools

See it in action

Watch the chain run from a raw crawl file to a tabbed Google Sheet of explicit LLM SEO recommendations, hands-off.

What it does

Three phases, one run. Keyword research feeds query generation, and query generation feeds the semantic audit. Three days of senior LLM SEO work compressed into a single chained workflow.

  • 12-step keyword discovery

    Pulls from titles, URLs, headers, competitor pages, People Also Ask, AI search and social signals, combined into one prioritised list.

  • Five long-tail queries per keyword

    Every approved keyword expanded into five LLM-search variants based on semantic behaviour.

  • Three-way scoring

    Each query scored on LLM-query likelihood, relevancy and search volume signal. Top opportunities surface automatically.

  • A five-category audit

    Final recommendations sorted into a tabbed Google Sheet: new content, update targeting, add FAQ, restructure, or other.

The workflow

Three phases that chain. Each phase’s output is the next phase’s input, and the only manual checkpoint is between the first two.

  1. Keyword research

    Combine business context, target areas and a fresh crawl file into a prioritised, categorised keyword list, ready for review. The user approves this phase before the chain continues. It is the only manual checkpoint in the whole workflow.

    What runs inside this phase
    Starting inputs
    Crawl file, business context, target areas, competitor URLs.
    Flows to query generation
    An approved keyword Google Sheet.
    • 12-step combineCrawl file, business context and target areas combined using a 12-step approach to seed the initial keyword universe.
    • Multi-source pullKeywords pulled from titles, URL inclusion, header structure, competitor pages, People Also Ask, AI search behaviour and social.
    • Prioritise and categoriseThe full list is deduplicated, scored and grouped into topical categories so the most valuable keywords surface first.
    • User approvalThe filtered list is presented for review. Once approved, the system generates a shareable Google Sheet for phase two.

  2. Query generation

    Turn each approved keyword into five long-tail LLM queries, score each one on three signals, and map them back against the crawl file to see what your site already covers and what it doesn’t.

    What runs inside this phase
    Flows from keyword research
    The approved keyword Google Sheet.
    Flows to the semantic audit
    An updated sheet with keywords, queries and prioritised LLM queries.
    • Ingest keywordsThe approved keyword document is parsed and prepared for semantic expansion.
    • Five long-tail variantsFive LLM-search variants generated per keyword from real semantic behaviour, not template substitution.
    • Three-way scoringEach variant scored on LLM-query likelihood, topical relevancy and search volume signal.
    • Map to the crawl fileFinal LLM queries mapped against your existing crawl to show what your site already covers and where the gaps are.
    • Updated sheetAn updated Google Sheet with keywords, queries and a prioritised LLM query list, ready for audit.

  3. Semantic audit and recommendations

    Take the full set of scored LLM queries, compare every one against the site mapping, and sort opportunities into five action categories. Each category gets its own tab in the final sheet, with explicit recommendations.

    What runs inside this phase
    Flows from query generation
    The full set of scored, mapped LLM queries.
    Final deliverable
    A tabbed recommendations sheet with explicit guidance per opportunity.
    • Ingest queriesThe full LLM query set is loaded into the audit engine with all mapping context.
    • Compare to the mappingEach query checked against the crawl-to-query mapping for coverage strength and content fit.
    • Sort into five categoriesNew content required, update targeting, add FAQ, restructure on-page, or other.
    • Tabbed recommendationsEach category gets its own tab in the Google Sheet, with named recommendations and the reasoning behind each one.

The problem

LLM optimisation was three days of senior expertise. Each phase was its own engagement, mistakes cascaded downstream, and junior staff were locked out.

LLM SEO is genuinely hard. Keyword research alone takes a day if you do it properly: pulling from competitors, People Also Ask, AI search behaviour, social signals and your own crawl, then prioritising the mess into something usable. Query generation takes another day, turning every keyword into the variants real users and LLMs actually search. The semantic audit, comparing queries against your site map, sorting opportunities and writing explicit recommendations, takes a third.

It scaled badly. Each phase was its own engagement and a junior couldn’t run any of it alone. A small mistake in phase one would silently compound through phases two and three, only surfacing at the end of three days.

  • 3 days of senior time for a single LLM optimisation audit.
  • Three separate engagements stitched together by hand, each with its own quality risk.
  • A phase-one miss became a phase-three problem.
  • Junior staff couldn’t deliver on their own, capping how many audits the team could run.
“By the time we were three days in, no one wanted to look at it again. Even when it was right.”

The solution

A single automated chain. Three phases, each with its quality gate built in, each handing its output directly to the next.

One chain, three phases, an hour instead of three days

The chain runs keyword research, query generation and the semantic audit back to back. Phase one’s approved keyword sheet feeds straight into phase two, and phase two’s scored queries feed straight into phase three. The output is a tabbed Google Sheet with explicit recommendations in five action categories. The user approves once, between phases one and two; the rest runs hands-off.

  • Multi-source keyword engineCrawl, context, competitor, People Also Ask, AI and social signals combined into one prioritised list using a 12-step approach.
  • Semantic query expansionFive long-tail LLM variants per keyword, scored on likelihood, relevancy and search volume signal.
  • Tabbed audit outputEvery opportunity sorted into one of five action categories, each with its own tab and explicit recommendations.
LLM optimisation chainExample data
  1. Keyword research412 keywordsApproved
  2. Query generation2,060 queriesScored
  3. Semantic audit134 actionsDone
Sheet tabActionsShare
Update targeting54
New content38
Add FAQ21
Restructure12
Other9
Crawl file in, recommendations sheet out52 min

Want to see it?

Arrange a call and we will walk you through this chain live, plus the adjacent workflows we could build for your team.

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The outcome

Three days became one hour. Audit quality lifted because every recommendation is anchored to the same chain of evidence, and junior staff can now run the whole thing.

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Your three-day audit, in an hour

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