Multi-LLM Probing.

“A brand that resolves cleanly in Claude and fails in ChatGPT doesn't have an 'AI visibility problem' — it has an entity-consistency problem that compounds differently across training corpora.”

— The Patnick perspective

What is multi-LLM probing?

Multi-LLM probing is the technique of submitting identical entity-targeted queries to multiple large language models in parallel — ChatGPT, Claude, Gemini — and comparing how each one resolves the query to an entity. It produces a cross-model measurement that reveals your true brand presence across AI answers, not just your performance in one model's idiosyncratic corpus. Grounded in research on entity-oriented search and the Google patents covering knowledge graph integration and entity resolution.

Why probe multiple LLMs instead of just ChatGPT?

Different LLMs have different training data, alignment fine-tuning, and entity resolution maps. A brand firmly embedded in Claude's training data can be invisible in Gemini's, and vice versa. Published case studies on multilingual brand scaling demonstrate this empirically: entity semantics persist across corpora but surface representation varies dramatically. Measuring only one model gives you a 33% view of real AI search presence — and worse, you can optimize for quirks of one corpus while neglecting the entity consistency that compounds across all three.

How does Patnick parse LLM responses?

With a deterministic parser — regex, substring matching, sentence-window sentiment scoring — never with another LLM. Using an LLM to parse an LLM response would introduce measurement drift: every time a model updates, your parsing accuracy shifts and historical comparisons become meaningless. Deterministic parsers give you reproducible, stable metrics that survive model releases. This matters because every time-series metric in the 3-score model depends on consistent parsing across weeks and months of probe data.

What does Patnick extract from each probe?

Entity mention (boolean), entity position (ranked list index), citation (URL back to your domain), sentiment polarity (-1 to +1), competing entities (array), token counts, cost, and duration. The raw response text is stored for audit and re-parsing. This data feeds the 3-score model (Demand, Clarity, Saturation) and the cross-LLM consensus calculation.

What's the difference between probing and scraping?

Scraping reads existing web pages. Probing sends a query to an LLM and captures its generated response — which didn't exist a second before the probe. Probing measures what the LLM tells a real user right now, which is the actual user experience. Scraping SERPs measures your blue-link rank. In an entity-oriented search era, the first measurement matters far more than the second.

Does Patnick probe Perplexity?

Not by default. Perplexity is a search-grounded system — it reads live web results and summarizes them. That measures your SEO + retrieval ranking, not your LLM entity representation. Patnick deliberately focuses on non-grounded models (ChatGPT, Claude, Gemini) where responses come from the model's internalized knowledge. This gives you a different (and more stable) visibility signal that isn't affected by daily SERP fluctuations.