The research highlights a performance gap between generative models and LLM-driven panels. While LLM-based approaches often struggle with accuracy, models grounded in validated human data—termed generative data models—showed significantly higher consistency. To address this, Burke introduced the FAR Framework, a rubric designed to audit data quality across three specific dimensions: Fidelity, which checks alignment with source truth; Authenticity, which measures the presence of realistic variation; and Resolution, which evaluates how well variable relationships and business conclusions are preserved.

Eli Moore, Vice President of Data Strategy, emphasized that the true test of any synthetic model is its ability to mirror the conclusions reached through direct customer engagement. The firm argues that organizations must move beyond the superficial appeal of synthetic voices and prioritize proven decision-grade outcomes. Through its Burke Labs division, the firm aims to establish benchmarks that help companies distinguish between viable AI-driven insights and experimental tools that may compromise strategic planning.