LLMs are reciting outdated positioning
ChatGPT describes your brand using 2022 messaging. The product has changed, the market has moved, but the LLM's training corpus is frozen. You can't just tell it to update.
Solutions · By RoleYour brand is a story.
Your brand is a story.
LLMs are retelling it without you.
You've spent years shaping your brand story — tone, positioning, competitive differentiation. Then ChatGPT and Claude started answering brand questions their own way, based on training data you never controlled. Patnick measures exactly how LLMs portray your brand, which competitors they surface instead, and what entity signals we can fix to course-correct the narrative.
Patnick for Brand Managers applies the 8-dimension audit with a brand-entity focus: Business Identity (how LLMs resolve and describe your brand), Trust & Proof (review sentiment + citation quality), Structured Data (Organization + Person + sameAs entity graph), plus AI Visibility layer (multi-LLM brand probing + forward-looking query simulation). You see exactly what ChatGPT, Claude, and Gemini say about your brand when nobody's looking.
This is why you're here.
Competitors surface when buyers ask about you
A buyer asks ChatGPT 'is [your brand] better than [competitor]'. The answer recommends the competitor. Not because they're better — because their entity signals are cleaner and their reviews are better structured.
No way to measure brand visibility in AI
Traditional brand tracking (surveys, social listening) doesn't measure the LLM layer. You know your share of voice on Twitter. You have zero idea what Claude says when someone asks about you.
Here's what I do about it.
Each capability is a piece of my 8-dimension audit. Click any of them to see how it works in depth.
What changes after the first audit.
0
LLMs probed monthly
ChatGPT · Claude · Gemini
0
Dimensions audited
Brand-weighted framework
0+
Brand-intent queries probed
Per site per month
What I'll fix in the first month.
- Multi-LLM brand baseline (current state of your entity in each model)
- Competitor share-of-voice in each LLM for your category queries
- Organization + Person schema deployed with proper sameAs
- Brand review schema (AggregateRating, Review) audit
- Forward-looking query probing for emerging category conversations
- Wikipedia + Wikidata entity check (LLMs sample both heavily)
- Brand citation tracking — who in the LLM training data cites you
- Monthly brand visibility PDF for exec distribution
See it in the dashboard.
patnick.com/dashboard
Frequently asked questions.
- Does this replace traditional brand tracking?
- No — it complements it. Traditional tracking (surveys, social listening, NPS) measures what real humans say about your brand. Patnick measures what AI says about your brand. Both matter, and they can diverge dramatically. A brand can have 80 NPS but be invisible in ChatGPT.
- Can you change what LLMs say about us?
- Directly — no. LLM training is fixed until the next model release. Indirectly — yes. Model providers retrain periodically, sampling from current web data. If we fix your entity signals, schema, and citation density now, the next model release will encode the improved representation. Time horizon: 3-12 months.
- What if an LLM says something incorrect about our brand?
- I document it, track how widespread the misinformation is across models, and recommend corrections we can push through structured data + clean citation signals. I'll flag any issue where legal action might be warranted (defamation, factual errors affecting safety) but Patnick itself doesn't do takedown requests — that's your legal team.
- How do you handle multi-market brand entities?
- If your brand operates in multiple countries with different legal names (Inc, GmbH, Ltd), I set up cross-lingual sameAs references so Google's knowledge graph resolves them as one entity. See /solutions/multilingual for the full multi-market approach.
- What's the difference between this and Cognizo or Profound?
- They only measure current queries. Patnick also probes forward-looking queries (emerging 2026-2027 category conversations) which gives you first-mover positioning before buyers even know to ask. Plus the 3-score model is transparent — Demand/Clarity/Saturation with published formulas, not black-box composite.
- Is this just for large brands?
- No. Small and mid-market brands often benefit MORE because your entity signals are weaker by default, so fixes produce larger deltas. If your brand is at risk of being drowned out by bigger competitors in LLM answers, this is urgent work.
Pricing
One price. One website. Everything personalized.
I work with you on either plan. At $499/mo, I build the roadmap and your writer executes. At $799/mo, I handle everything end-to-end.
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Ready to start?
Start your audit or talk to me directly. I respond to every inquiry personally.