Relying on general-purpose LLMs for high-stakes healthcare analytics is a strategic risk. While these models are highly fluent, they lack the logical reasoning and clinical context required to navigate complex patient data without generating “methodological hallucinations.”

Our latest white paper benchmarks Marmot™ against leading foundational models across core commercial workflows. The results are clear: data access alone is insufficient without a specialized semantic layer to interpret it.

Download the report to learn: 

• The Accuracy Gap: Why Marmot consistently achieved ~80% accuracy while foundational models struggled with a failure rate of nearly 44%
  

• The “Volume vs. Value” Trap: How generic AI mischaracterizes HCP targeting by equating raw counts with actionable value

• The Five Layers of Defense: How Komodo’s architecture wraps AI in clinical context, auditable tools, and strict business logic to deliver results you can trust