AI Search Visibility
llms.txt Implementation
Most llms.txt files are auto-generated and ignored. Strategic content prioritisation, validated implementation, and ongoing maintenance designed for citation in ChatGPT, Perplexity, Claude, and Google AI Overviews.
Deliverables
Content prioritisation map
A documented map of which pages and content sections matter most for AI Search engines, with reasoning behind every choice.
llms.txt file drafted and deployed
Written to specification, validated against the standard, deployed to your domain root and confirmed accessible to AI engines.
llms-full.txt extended file
Where appropriate, the expanded version that surfaces deeper content for AI engines that ingest it.
Citation tracking setup
Baseline measurement and ongoing tracking for AI Search citations across ChatGPT, Perplexity, Claude, and Google AI Overviews.
Maintenance playbook
A documented process for keeping llms.txt current as you publish new content. Built for your team to run with.
Written documentation
Full handover docs your team can maintain after the engagement closes. No black boxes.
Process
Discovery call
Thirty minutes. We walk through your site structure, your content strategy, your AI Search goals, and your current llms.txt state if any. By the end I know what scope fits.
Content audit and prioritisation
Two to three weeks. Mapping which pages, sections, and content types matter most for AI Search engines. Output is a prioritisation document that informs the llms.txt draft.
Drafting
I write the llms.txt file to specification, validated against the current standard. Where relevant, I also draft the llms-full.txt for deeper content surfacing.
Implementation
Deploying the files to your domain root. Validation in production. Confirming AI engines can access them and read them correctly.
Tracking baseline and handover
Citation tracking baseline across ChatGPT, Perplexity, Claude, and Google AI Overviews. Documented handover so your team can maintain the file going forward.
Packages
Strategy & Draft
From £1,800
Content prioritisation and a drafted llms.txt
2 to 3 weeks
- Content audit and prioritisation map
- llms.txt drafted to current specification
- Validation report against the standard
- Deployment guide for your team
- Recommendations for what to include in a future llms-full.txt
Full Implementation
From £4,200
Strategy plus llms-full.txt, deployed and tracked
4 to 6 weeks
- Everything in Strategy & Draft
- llms-full.txt drafted for deeper content surfacing
- Deployment to your domain root
- Citation tracking baseline across ChatGPT, Perplexity, Claude, and Google AI Overviews
- Written handover documentation
Ongoing Maintenance
From £700/month
llms.txt that stays current as you publish
Quarterly cadence
- Quarterly llms.txt reviews and updates
- Coverage for new content and sections
- Citation tracking and reporting
- Specification monitoring (the standard is still evolving)
- Direct email access for llms.txt questions
Case Studies
Halewood Editorial
Strategic llms.txt for a large editorial archive
Editorial site with a 1,200-piece article archive had no llms.txt and was effectively invisible to AI Search prioritisation. Mapped priority topics across the archive, drafted the llms.txt with strategic content surfacing, then added llms-full.txt for the highest-priority pieces.
Outcome:
Cendric
llms.txt designed alongside schema work
B2B SaaS with extensive product documentation but no AI engine guidance. Designed an llms.txt that surfaces their pricing pages, product features, and core documentation explicitly. Implemented alongside their existing schema work for a coherent AI Search layer.
Outcome:
Aldernode
Selective llms.txt for product surfacing
E-commerce catalogue with 1,200 SKUs needed selective AI Search surfacing - not every product page, just the ones that matter. Built a category-prioritised llms.txt with strategic surfacing rules, validated against the spec.
Outcome:
FAQs
What is llms.txt and why does my site need one?
llms.txt is a markdown file placed at the root of your domain (yoursite.com/llms.txt) that tells AI search engines what content matters most for citation. ChatGPT, Perplexity, Claude, and Google AI Overviews increasingly use these files to prioritise what they surface. Without one, you rely on AI engines guessing what matters on your site, which they often get wrong.
How long does implementation typically take?
Strategy and draft: two to three weeks. Full implementation including llms-full.txt: four to six weeks. Larger editorial or multi-brand sites may run longer. Ongoing maintenance runs on a quarterly cadence with email check-ins.
What is the difference between llms.txt and llms-full.txt?
llms.txt is a navigation-style file with priority sections and links. llms-full.txt is the expanded version that surfaces full content for AI engines that ingest it. Not every site needs both. We decide during discovery based on your content strategy and scale.
Will AI engines actually read my llms.txt file?
Yes, increasingly. The major AI engines (ChatGPT, Perplexity, Claude) have signalled support or implemented support for the standard. Adoption is uneven across engines and the standard is still maturing. What I optimise for is the trajectory: files that work today and will work better as adoption deepens.
Do you need developer access to deploy?
Usually no. llms.txt files are placed at your domain root. Most hosting setups let you upload a file directly. I draft and validate; your team or hosting provider deploys. We confirm the workflow during discovery.
How do you measure success?
Three layers. First, "validation": the file passes against the current specification. Second, "accessibility": AI engines can fetch the file from your domain. Third, "citation": tracked appearances in ChatGPT, Perplexity, Claude, and Google AI Overviews. All three reported in the deliverable.
The standard is still evolving. What happens when it changes?
I track the specification through proposals and community discussion. I publish on this topic regularly at the Technical SEO Library. When the standard changes meaningfully, I update existing client implementations as part of maintenance engagements, so you do not get stuck on a deprecated version.
Do I keep the documentation and files?
Yes. Everything I deliver is yours: the llms.txt file, the audit document, the prioritisation map, the maintenance playbook. Your team can run with it after the engagement closes.