Track your brand in Llama
Llama is Meta's family of open-weight models: the engine behind Meta AI in WhatsApp, Instagram, Facebook, and Messenger, and the most widely deployed open model family in the world. What Llama believes about your brand is repeated across billions of chat surfaces and thousands of third-party products. Diploria tracks it directly.
How Llama decides who to mention
Llama's answers about brands come from training memory: the picture of your brand assembled from the open web before each model's training cutoff. Broad, consistent coverage across reviews, articles, forums, and comparison content is what earns a confident mention.
That memory is frozen between releases. New products, rebrands, and repositioning since the cutoff simply do not exist in a bare Llama model's answers, no matter how good your current website is.
Distribution does the rest. Meta AI puts Llama in front of billions of users inside Meta's apps (where Meta layers its own retrieval on top), while countless third-party deployments run the open weights as-is, serving pure model memory. The same trained-in brand picture flows through all of it.
Want the deep dive? How AI engines choose which brands to mention.
What Diploria tracks in Llama
- Whether Llama mentions your brand on each tracked prompt
- The full text of every response, stored, so you can read how it describes you
- Sentiment on each mention
- Competitors named on the same prompts and your share of voice
- Accuracy: where Llama's trained-in picture of your brand lags or errs
- Shifts across model releases, when the memory actually changes
What makes Llama different
Llama's unique property is reach without retrieval. It is the default open model for the industry: startups, enterprises, and Meta's own consumer apps all build on it, and most of those deployments answer from model memory alone. That makes Llama the best single proxy for the question 'what does the open-source AI ecosystem believe about my brand?'. The training cutoff is the defining constraint: your visibility in Llama today was determined by your web footprint months or years ago, and the work you do now pays out at the next release. Diploria is the only visibility platform that measures any of this.
How to improve your visibility in Llama
- Build durable, high-authority coverage that survives into training crawls: reviews, comparisons, reference content
- Correct inaccuracies about your brand on prominent pages now, so the next training run absorbs the fix
- Keep naming and positioning consistent everywhere; contradictions produce vague memory answers
- Use Diploria to audit what Llama currently gets wrong, and brief your team on what downstream apps are repeating
- Re-measure at each new Llama release to verify the trained-in picture moved
Diploria's Optimization Hub turns this into a workflow: the AI Readiness Scan grades any page for AI readiness, the AI Readiness Checklist prioritises the fixes, and AI crawler access checks make sure engines can reach your content at all.
Llama tracking questions
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