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.

Ground truth comparison: When users upload CRM notes, Gong call transcripts, or win/loss data, we benchmark the twin’s language and objections against what real prospects actually said.

Confidence scoring: If a twin sounds overly confident but doesn’t have enough supporting data (e.g., limited context or sparse history), the system flags it with a lower reliability score rather than pretending it’s certain.

Iterative calibration: Each feedback cycle — whether a message worked or not — helps fine-tune the twin so its “voice” and reasoning evolve over time.

The end goal is that twins shouldn’t pretend to know — they should learn continuously from every interaction and new data point.