AI gives one answer.
We make it name you.
Search stopped being ten blue links. Buyers ask ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews — and act on whichever brand the model recommends. Aurvikon measures where you stand across all five engines, then fixes what it takes to become the answer.
Modern AI answers by retrieving, not remembering
Retrieval-Augmented Generation is how ChatGPT-search, Claude, Gemini, Perplexity and Google AI Overviews answer current questions. Understand these five steps and you understand exactly where a citation is won — or lost.
Ask
A buyer types a real question to the AI.
Retrieve
It searches the live web + a vector index and pulls the top matching chunks.
where you winAugment
Those chunks are injected into the model’s prompt as sources.
Generate
The model writes the answer, grounded in what it retrieved.
Cite
It names the sources it used — the moment you exist, or don’t.
You get cited only if you win retrieval
The model can only recommend what it pulled in. Three things decide whether your page is in that shortlist — and all three are things you can change.
Retrievable
Crawlable, indexed, and present in the places these engines actually pull from — your site, docs, and the third-party sources (Reddit, G2, comparison pages) they trust.
Semantically matching
Your content has to mean the same thing as the question. Retrieval is embedding-based — the closer your page sits to the buyer’s intent in vector space, the higher it ranks.
Liftable
A clean, self-contained answer the model can quote verbatim — direct claims, specific facts, structured markup — beats a paragraph it has to untangle.
Every step of RAG maps to something Aurvikon does
This is why the platform isn’t a dashboard — it’s a lever on each stage where the citation is decided.
We reconstruct the exact questions buyers ask AI about your category — so you optimize for real demand, not guesses.
We show which sources each engine cites, whether you’re retrievable, and how close your content sits to the question in vector space.
We rewrite passages to lead with a direct answer and re-embed them to measure how much more retrievable they’ve become — then deploy the fix to your CMS.
Every scan checks whether you were actually named — per engine, per market — and alerts you the moment your standing shifts.
One closed loop, run continuously
Measure
Scan 5 engines — where you’re named, where you’re invisible.
Diagnose
Find the retrieval gaps: citations, schema, content, intent.
Fix
Generate the content, schema and rewrites that win retrieval.
Deploy
Push the fix live to your CMS — no copy-paste.
Re-measure
Rescan and prove the citation moved.
We maximize the odds you’re the answer.
We don’t fake a guarantee.
No one can force a model’s choice. What we can do is make you the best source its retrieval can find — the strongest, most liftable, most-cited answer to the questions your buyers actually ask. In practice, that’s how you win.
// RAG decides who gets cited. Aurvikon makes you the source it keeps choosing.