AI SEO or GEO building ideas

[Joe Pracher]

I have to give you massive credit here, your diagnosis of the legacy tech bloat is spot on. A 70% 'Poor' PageSpeed rating across the industry is embarrassing, and building a clean, SSR-first infrastructure with pristine schema is exactly what the industry needs to fix the crawl budget issues. I am genuinely looking forward to seeing those technical benchmarks for @DealerInt.

Where I think the architecture still hits a wall is your assumption about how non-Google models acquire data, specifically regarding retail velocity versus index churn.

You mentioned that GMC feeds and SSR schema are how Perplexity and OpenAI 'know' a car arrived. GMC is fantastic for Google's ecosystem, but Google isn't sharing that structured feed with OpenAI or Anthropic. For those models to discover inventory without an aggregator, they are entirely reliant on their own web crawlers hitting your SSR schema.

Even with a lightning fast site, standard web crawling is fundamentally incompatible with automotive retail velocity. While an average unit might sit for 30 to 60 days, the highly desirable, aggressively priced inventory....the exact cars users are actively querying AI for, often move in a matter of days. If a foundational model's crawler only indexes a specific VDP once a week, the AI is going to confidently send shoppers to 404 pages and sold vehicles. Models cannot tolerate that level of hallucination risk.

The 'Aggregator Tax' isn't just a visibility tax; it's a data-licensing reality. Foundational models are striking massive enterprise data deals with centralized hubs precisely because they need a real-time API firehose, not because they want to rely on crawling decentralized local domains, no matter how fast or clean your pipe is.

I completely agree that your infrastructure will absolutely crush legacy platforms on standard Google crawlability. But until OpenAI decides to trust and query thousands of decentralized MCP endpoints instead of buying a clean, normalized data feed from a centralized network, the aggregators still hold the keys to the non-Google LLM intelligence layers.

 

[DealerInt]

Joe — fair clarification on the GMC distinction. To be precise: GMC is the discovery signal for Google's ecosystem specifically. What I should have said is that SSR-rendered structured schema on the VDP pages themselves is what non-Google crawlers directly consume. Same infrastructure argument, tighter mechanism.

But on inventory velocity — I'd argue our earlier conversation actually answers this. The MCP tool-call layer isn't a crawl solution. It's a real-time verification layer. The crawl solves discovery — does this dealer carry this type of inventory, are they local, are they credible. VDP-level precision — is this specific unit still available at this exact price right now — is exactly what the bottom-of-funnel MCP execution handles.

A direct tool-call to the dealer's live database at the moment the agent needs the answer, not a week-old crawl snapshot. So the failure mode you're describing isn't: crawl inventory get 404s. It's incomplete architecture where the execution layer is missing. Discovery via clean SSR pipe MCP tool-call verifies live status before the recommendation fires. That's the hallucination problem solved at execution, not index.

On the data-licensing deals — they exist today, agreed. But those deals are precisely what WebMCP and OpenAI's MCP announcement are designed to disrupt long-term. The aggregators hold the keys right now because the alternative infrastructure isn't at scale yet. The clean pipe is the prerequisite for that shift — not the solution that waits for it. Looking forward to sharing those crawl-latency benchmarks here when they're ready. That's where the infrastructure argument becomes empirical rather than theoretical.