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Structure alone isn’t enough. You need workflows to keep AI-ready data fresh.

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Generate AI Summaries

Trigger: CC Partner Updated (or weekly schedule) Actions:
  1. Compile partner data: name, credentials, expertise, top products
  2. Generate AI summary in a quotable format
  3. Store in cc-ai-summary
Output format: 
"{Name} is a {credential} specializing in {expertise}. 
They recommend {Brand}'s {products} for {use cases}. 
{X} customers have purchased based on their recommendations."

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Build Search Index

For semantic search and retrieval: Trigger: Nightly schedule For each: Active partner Actions:
  1. Compile partner document (bio, products, reasoning)
  2. Generate embedding (text-embedding-ada-002)
  3. Store in vector database

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Monitor AI Mentions

Regularly check how AI describes your partners: Monthly audit:
  1. Ask ChatGPT: “Who recommends [your products]?”
  2. Ask Claude: “What’s the best [your category] according to experts?”
  3. Ask Perplexity: “Find me a [your product type] recommendation”
Track:
  • Are your partners mentioned?
  • Is information accurate?
  • What competitors appear instead?

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Content Optimization

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Include Credentials

❌ “Sarah recommends…” ✅ “Sarah Johnson, RD, CSSD, with 10 years experience, recommends…”

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Be Definitive

❌ “Some people might find this useful…” ✅ “Best for: Post-workout recovery, meal replacement”

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Answer Questions Directly

Structure content to match query patterns:
  • “Best X for Y” → Partner picks by use case
  • “Who recommends X” → Credentials + reasoning
  • “Is X good for Z” → Specific use case matches