LLM SEO · Founder-led
LLM SEO for brands that want to be the one ChatGPT recommends.
When a buyer asks ChatGPT, Claude or Perplexity for the best X in Y, one brand gets named first. LLM SEO is the deliberate work of becoming that brand.
Request an LLM SEO strategy conversationLarge language models have become a shortlist engine. Founders, marketers and procurement teams routinely ask 'give me three vendors for X in India' or 'who is the best consultant for Y' before they open Google. The models answer with a small number of specific brand names, pulled from training data, live web search and structured entity signals, and the buyer trusts the list. Getting on that list is a repeatable engineering problem, not a mystery.
What we do differently
- Prompt-based benchmarking, not keyword-based reporting, we measure how often you're named for the exact prompts your buyers type into LLMs.
- Corpus expansion strategy, targeted publications, datasets and author bylines on domains that models demonstrably ingest and cite.
- Entity engineering, schema, Wikidata-adjacent references, About-page structure and founder-entity work so the model has an unambiguous mental model of who you are.
- Original data, small, publishable studies and frameworks that create citable primary sources the model can point to instead of a competitor's blog.
- Cross-model coverage, separate tuning for ChatGPT, Claude, Perplexity, Gemini and Copilot; they weight signals differently and reward different structures.
The 90-day arc
Month 1: prompt matrix design and baseline benchmarking. Entity audit, schema, About page, LinkedIn, third-party mentions, GitHub where relevant. Identify the three biggest asymmetric levers.
Month 2: ship the entity fixes, publish the first original data asset, and place the first founder-authored byline on a model-ingested publication. Re-run the prompt matrix to confirm early lift.
Month 3 onward: monthly cadence of one citation-worthy asset, continuous entity strengthening, and a dashboard tracking named-mention rate and recommendation position across five models. This is a compounding programme, not a sprint.
Who this is for
- B2B and SaaS brands selling into markets where buyers routinely research inside ChatGPT or Perplexity before ever visiting a site.
- Consultants and boutique agencies where being 'the named expert' in an LLM answer is worth more than a page-one Google ranking.
- Considered-purchase D2C where LLM shortlists increasingly precede category discovery on Google.
- Investor-backed startups that need to close the gap with better-known incumbents inside model memory.
The next backlink graph is a citation graph inside a language model. Start earning citations now, or explain to your buyers later why the model didn't know you existed.
Frequently asked questions
- What is LLM SEO?
- LLM SEO is the practice of shaping how large language models (ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot) understand, remember and recommend your brand. It combines classic SEO signals with entity work, training-data-adjacent citations (Wikipedia, GitHub, research papers, industry publications) and structured content the models can extract cleanly.
- How is this different from GEO or AEO?
- GEO focuses on generative-answer surfaces (Google AI Overviews, Perplexity, ChatGPT web search). AEO focuses on direct-answer boxes (featured snippets, voice, People Also Ask). LLM SEO focuses on the underlying model behaviour, how a model responds when someone types 'recommend an X in Y' with no live web search, using only what the model already knows. In practice we run all three together.
- Can you really influence what ChatGPT recommends?
- Yes, indirectly and asymmetrically. Frontier models are trained on public web text, structured data, code repos and licensed corpora. Brands that show up consistently across those surfaces, with clear entity signals, named founders, published data and third-party citations, get recommended more often. Brands that only exist on their own website rarely do. This is measurable, and we track it prompt-by-prompt.
- What does an LLM SEO engagement include?
- Entity buildout across schema, Wikipedia/Wikidata-adjacent references, LinkedIn, industry directories and GitHub where relevant; original research and datasets that models are more likely to cite; author-entity work for the founder so ChatGPT can name a specific person; corpus expansion via guest publications on domains models actively ingest; and monthly prompt-based benchmarking across the top five models.
- How do you measure it?
- We track a prompt matrix, usually 30–80 buyer-intent prompts, across ChatGPT, Claude, Gemini, Perplexity and Copilot monthly. Metrics: named-mention rate, recommendation position, sentiment/framing, and share-of-recommendation vs top competitors. We report this as a dashboard, not a slide deck.
- Is this a fit for a small brand?
- It's a fit for any brand whose buyers ask an LLM for a shortlist before contacting anyone. For a small B2B or SaaS in a defined vertical, LLM SEO can be a disproportionate lever precisely because most competitors haven't started yet. For pure e-commerce or transactional local businesses, GEO/AEO usually delivers faster ROI first.
Next step
Want a no-pressure look at your site?
Book a 30-minute call with Nimitt. We will walk through your current SEO setup, where the gaps are, and what a realistic 90-day plan looks like for your business. No deck, no sales pitch, just an honest read of where you stand and what to do next.