This paper, written by Gaine's CEO, makes the case that data architecture is your most important AI cost lever — and that the decisions you make now will determine whether you retrofit later at a far higher price.
What's inside
- The real economics of AI inference — what drives token consumption and why it compounds
- How data ambiguity in provider and member records forces AI to work harder on every query
- Why health plans face unique data quality challenges that generic AI deployments can't absorb
- The data architecture principles that reduce inference burden and improve model accuracy
- Why acting on data quality now is more cost-effective than retrofitting after AI is in production
- How Gaine HDMP serves as an operational control plane for clean, consistent data that AI can trust
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