Geeks Digital ChatGPT answers hundreds of millions of questions a week, and a growing share of them end with a recommendation: a product, a provider, a brand. ChatGPT Optimization is the focused work of making sure that when the recommendation is in your category, it is you.
What actually influences ChatGPT
Three layers, each addressable. The model’s training memory, formed from the public web: it rewards a consistent, corroborated record about your brand across sources it ingested. Live browsing, used when recency matters: it rewards pages GPTBot can fetch and passages it can quote verbatim. And retrieval inside answers with search enabled, which leans on Bing’s index more than most people realize, making Bing visibility quietly strategic again.
What we handle
- Prompt-panel baseline: how ChatGPT describes you today across the questions your buyers actually ask, with competitor share tracked
- GPTBot access verification and llms.txt so OpenAI’s crawlers read your best pages instead of bouncing off a firewall rule
- Answer-shaped content: definitions, comparisons, pricing clarity and step sequences written so a single paragraph stands alone as a complete quote
- Bing index hygiene, because browsing-mode citations disproportionately draw from it
- Fact corroboration across the third-party sources the model treats as consensus, correcting outdated descriptions at their origin
- Monthly measurement of appearance rate, framing and citation share, reported like rankings
Our approach
We fix access first, because nothing else matters if the crawler cannot read you. Then we make your key pages quotable, then we build the external record that lets the model recommend you with confidence. The panel re-runs monthly; movement typically shows within one to two model-and-index refresh cycles once the groundwork ships.
Can you guarantee ChatGPT will recommend us?
No one honest can; the model is probabilistic and updates on its own schedule. What we control is everything that raises the probability: access, quotability, corroboration and freshness. The panel makes progress visible instead of anecdotal.
Does this help with other assistants too?
Substantially. The groundwork overlaps heavily with Gemini, Perplexity and Copilot visibility, which is why many clients run this inside the broader GEO program rather than alone.
The brand-question layer most teams forget
Buyers do not only ask for recommendations; they ask about you: is this company legit, what do they charge, who are their competitors, what do reviews say. ChatGPT answers all of it, today, from whatever record exists. Part of every engagement is owning that interrogation: publishing clear pricing posture, honest comparison content and a reference-grade About layer, so the model’s answers to due-diligence questions come from your framing rather than a forum thread from 2019.
Does ChatGPT Search change the playbook?
It strengthens the retrieval half: with search enabled, answers cite live pages, which makes fetchability, freshness and quotable structure pay immediately rather than waiting on training refreshes. The memory half still matters for default answers, so the two-track approach stands: fast retrieval wins now, record-building for durable presence.
Related depth: LLM Optimization for the model-memory layer and AI Citation Building for the external record. Track everything in one place with AI Search Visibility.
