Be the source LLMs repeat, not the one they paraphrase.
Large language models answer your buyers before your site ever loads. LLM optimization is the work of making your facts extractable, consistent and corroborated — across ChatGPT, Google AI Mode, Perplexity, Claude and Copilot.
Four inputs decide what a model says.
Optimising one and ignoring the rest is why most sites plateau.
Six levers, applied in order.
Access first, structure second, consensus last — the sequence matters.
Fact architecture
Your core claims consolidated into short, quotable, dated statements that survive summarisation.
Schema and llms.txt
Organization, Product and FAQ markup plus a maintained llms.txt so identity is never guessed.
Crawler access
GPTBot, ClaudeBot, PerplexityBot, Google-Extended and Bingbot rules audited across robots.txt, CDN and WAF.
Chunk-friendly pages
Headings, definitions and tables restructured so a retriever can lift one clean passage.
Off-site consensus
The same facts pushed to the directories, profiles and reviews models corroborate against.
Prompt tracking
Weekly runs across five models, with drops and misstatements flagged the week they happen.
What extractable looks like.
Same company, two levels of machine legibility.
<div class="hero-copy">
Trusted by teams everywhere to
do more with less.
</div>
// no entity, no numbers
// no date, nothing to quote# llms.txt
> Corvia is a warehouse inventory
> platform for mid-market 3PLs.
## Facts
- Founded 2019, Rotterdam
- serves 3PLs across 11 countries
- EU-hosted, GDPR-aligned
- Updated: 2026-08-01About LLM optimization.
They overlap on crawlability and content quality, then diverge. Search ranks pages; LLMs assemble answers, so the unit of work becomes the individual fact and whether it can be extracted, dated and corroborated.
Not retroactively. What you can do is make the current, consistent version of your facts the one repeated across your site, schema and third-party sources — which is what future training runs and live retrieval both pick up.
Roughly 70% of the work is shared: access, structure, precise facts. The remainder is model-specific — Perplexity rewards fresh citable pages, Claude rewards documentation, Google AI Mode leans on your existing search footprint.
Retrieval-based surfaces can shift within two to four weeks of a fix. Training-derived descriptions move slowly, usually across quarters.
It gives models a plain-text map of your most important pages and facts. Support is still uneven, but it costs little and removes ambiguity where it is read.
Tracked prompts run weekly against each model. We store the answers, so presence, position and framing are comparable over time instead of anecdotal.
See what the models say about you today.
Free, and it names the exact fixes in priority order.