Thanks — that’s exactly the problem we’re trying to solve. Traditional personas go stale fast, and most survey data is self-reported, not behavioral.

On hallucinations and bias: We handle it in three ways right now —

Grounding in real data: Each twin is built using structured + unstructured data (LinkedIn profiles, CRM notes, messaging, etc.), so the LLM has contextual grounding rather than free-form guessing.

Feedback calibration: Every time users compare twin feedback with real user insights (e.g., call transcripts or campaign results), that feedback loop fine-tunes how the twin weighs language patterns and priorities.

Cross-model validation: We run prompts through multiple models and look for consensus — if the outputs diverge too much, the system flags it for review rather than showing one “confident” but wrong answer.

It’s still early, but the goal is to make twins that drift with real customer data — not just sit frozen like static personas.