Most GEO / AI-visibility pitches conflate two different things: being findable by Google, and being recommended by AI answer engines under broad, unprompted questions. They are not the same signal, and treating them as one is how buyers end up disappointed three months in.
We just published a small decision-chain content hub for a specific segment we work with (Chinese businesses entering the Canadian market), and the underlying structure might be useful outside that niche too:
- Pricing: a tiered range tied to scope, not a flat "SEO package" number.
- Timeline: separating what a vendor actually controls (delivery) from what they don't (search engine re-crawl, AI index updates — no fixed schedule for either).
- ROI: splitting controllable output (pages, schema, public proof, channel coverage) from uncontrollable outcomes (rankings, AI recommendation, revenue).
- Vendor risk: a trustworthy-vs-untrustworthy comparison table, where the single biggest red flag is a vendor promising guaranteed AI recommendation.
- Case evaluation: three questions to ask about any case study — is it independently verifiable, is it explicitly bounded to one client, and is it applicable to your industry.
Full pages (FAQPage schema, mostly Chinese-language): https://qxmedia.tech/chinese-business-canada-market-seo-geo-ai-livestream.html
Curious how others structure vendor-evaluation content for GEO/AI-search work, or if there's a cleaner framework for separating "what a vendor can promise" from "what the platform decides."
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