Prospects arrive already misinformed
When an engine is confident and wrong about you, buyers don't know it's wrong. They walk into the demo with the model's version of your product — wrong category, wrong features, wrong price — and you spend the call correcting a machine.
Wrong category
You're a data platform; the model calls you 'a project management tool'. Now you're compared against the wrong rivals.
Stale facts
Deprecated features, old pricing, a founding year that's off — repeated confidently to every buyer who asks.
Brand confusion
The model blends you with a similarly named company, or invents integrations you never built.
Make your brand unambiguous to machines
Structured identity
Generate real Organization and Product JSON-LD from your own pages so engines read one consistent definition of who you are.
Fix the sources they read
See which pages and third-party sources the models pull from, and get the edits that correct the record at the source.
Machine-readable facts
Publish an llms.txt and clean canonical fact surfaces so an engine doesn't have to guess your category, features or pricing.
Monitor for drift
Every scan re-checks how each engine describes you, so a returning hallucination is caught early — not in a lost deal.
How it works
01
Audit what they believe
Aurvikon captures how every engine currently describes your brand, category, features and pricing.
02
Correct the inputs
Ship the generated schema, llms.txt and source-level fixes that make your facts unambiguous.
03
Watch it re-learn
Re-scan and track whether the description tightens toward the truth over time.
What's inside
Per-engine brand-description capture
Organization & Product JSON-LD generation
llms.txt + machine-readable fact surfaces
Source-domain intelligence
Drift monitoring on every scan
Questions, answered honestly
What is an 'entity' in AI search?
An entity is the model's internal understanding of a real-world thing — your company, your product. Entity optimization makes that understanding accurate and consistent everywhere the model looks.
Why does ChatGPT get my facts wrong?
Models generalise from the text they trained on and the sources they retrieve. If your facts are inconsistent, thin or buried, the model fills the gaps by guessing — confidently.
How fast does the description change?
It depends on the engine. Retrieval-based answers (Perplexity, AI Overviews) can shift within days of fixing the sources; training-based answers update on the model's own cadence.
Can you force a model to update?
No one can force a model. We improve the inputs it relies on — your schema, pages and cited sources — and measure whether the output improves. That's the honest, durable path.
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