Sonder builds evidence-grounded digital personas of your customers and prospects from public data and CRM signals. Every behavioral assertion cites the records it came from. Every conversation respects a consent tier. No archetypes, no plausible fabrications — just calibrated, auditable understanding.
Demo workspace seeded with Freudenberg Sealing Technologies (Connie Sandros) and Sample Org (Sample Pat). No credit card; no signup wall.
The differentiation isn't more AI. It's the discipline that surrounds it.
Every claim about a customer carries a non-empty list of evidence record IDs. Confidence is derived from observable properties — count, recency, reliability, corroboration — not from a model's self-report. If there isn't a basis, the persona says so.
Observational, predictive, restricted. Tier is a property of the persona — checked at request time, never cached. Consent withdrawal downgrades and triggers recomputation. Restricted personas don't appear in surfaces they shouldn't.
Run a buying-committee simulation across the personas in a deal. Watch how a CFO, a champion, and a procurement lead each react to the same proposal — with the divergence itself becoming a coaching signal.
Sonder is one platform but four distinct layers. The L2 reviewer pipeline has no trusted-source bypass — a Salesforce log entry and a public LinkedIn post route through the same triage → extraction → scoring → routing flow. Source trust is an input to scoring, never a route around it.
Derived state is reconstructible. Delete a persona's evidence and the claims and narrative regenerate to match — by design.
The do-not list is structural — the platform refuses by construction, not by policy memo.
Sign in and open the demo workspace. Talk to Connie — the persona will refuse to speculate without evidence, and cite every claim it does make.
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