Right now, we validate twin responses in a few ways: Ground truth comparison: When users upload CRM notes, Gong call transcripts, or win/loss data, we benchmar

Right now, we validate twin responses in a few ways: 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.