3 ms·
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
by kashishkhanna55 11mo ago
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?
- resonaX 11mo agoRight 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.