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Interesting idea. Most “personas” I’ve seen just sit in Figma or Notion and don’t reflect how buyers actually talk anymore. If these twins update directly from CRM / LinkedIn data, that feels like a real step up from the usual marketing theatre.

One question: how do you validate when an AI twin gives a confident-sounding answer that isn’t actually what real prospects would say? Do you compare it against actual call notes or win/loss feedback?

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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.

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