| Primary question | How did each representative AI client reach its recommendation for this product? | How does the brand perform across answer engines, prompts, regions, citations, and competitors over time? |
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| Research method | On-demand matched buying journeys with model-directed web research. | Recurring prompts captured from consumer answer-engine interfaces, with aggregate analytics. |
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| Core output | A decision record with full responses, research paths, missed pages, competitors, and prioritized page changes. | Visibility and share-of-voice metrics, citations, sentiment, FactCheck, prompt intelligence, and content actions. |
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| Operating scale | Individual pages, products, and release iterations. | Brand portfolios, regions, languages, large prompt sets, and enterprise programs. |
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| Implementation loop | An OAuth MCP server lets a coding agent test, edit, deploy, and retest. | Agents support content generation and optimization within a broader AEO platform. |
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| Commercial fit | Self-serve plans from focused testing to agency portfolios. | Self-serve entry tiers plus tailored enterprise packages. |
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