The 2026 GEO checklist: 12 fixes that move the needle inside ChatGPT
Most brands obsess over a handful of vanity prompts. The winners fix the underlying signals — schema, knowledge graph, review velocity — and watch dozens of prompts move at once.
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“AI engines triangulate facts across Wikipedia, Wikidata, Crunchbase, LinkedIn, and your own site. If those disagree, the model picks the loudest source — rarely you.…”
FoundIn.ai Research · 8 min
- Research
ChatGPT vs Perplexity: why your citation rate is different on each
The two engines retrieve, rank, and cite brands in fundamentally different ways. Here's how to optimise for both without splitting your team.
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- GEO
What Google's AI Overviews really means for B2B pipeline
AI Overviews now sit above the classic results on a growing share of high-intent B2B queries. Here's what changes — and what doesn't.
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- GEO
Schema markup that actually moves the needle in generative engines
Which JSON-LD types ChatGPT, Perplexity, and Gemini actually parse — and which you can safely ignore.
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- Playbooks
A quarterly knowledge-graph hygiene routine for B2B brands
Wikidata, Crunchbase, and LinkedIn drift quietly. Here's a 45-minute quarterly routine to keep them aligned.
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- Research
Measuring share of voice inside AI engines
A pragmatic methodology for tracking your brand's slice of generative responses over time.
6 min readFoundIn.ai Research
- Playbooks
Why concentrating reviews on two sources beats spreading them
Generative engines weight a small set of review aggregators. Pick the right two for your category.
4 min readFoundIn.ai Research
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