The qualitative layer for your GTM stack.

The problem

The reasons on file usually aren't the real reasons.

CRM loss reasons are typed by a rep who's guessing. Exit surveys catch the first polite answer, not the real motive. Call recordings only hear your side of the table. The signal that's missing is qualitative — and capturing it at scale has been slow and expensive until now.

~85%

of CRM loss reasons don't match what the buyer actually says

2 in 3

exit surveys miss the real reason a customer churned

hours

not the weeks and agency budget a win-loss study used to take

Where it fits

Your pipeline counts the numbers. We capture the why.

The same interview engine plugs in at the three moments where motive matters more than metric — and each time it hands the evidence back to your stack.

What you can run

Four conversations, one engine.

• Win-loss interviews

  Learn why deals actually close — or slip away. The real motive, not the CRM label, on every deal you close.

• Churn interviews

  Catch the real reason at the moment someone cancels — before it flattens into a polite one-line survey.

• Problem discovery

  Validate pain, ICP, and positioning with real people before you build or launch — without leading the answer.

• Message & ICP testing

  Find out what actually resonates before you scale outbound — grounded in recent behavior, not opinion.

How it works

You bring the people and the question. We do the rest.

1. I

   Invite

   Send a single-use link to the people who know — a lost deal, a churned user, an early adopter.

2. II

   Interview

   The agent runs an adaptive conversation that gets past politeness, anchors vague claims to specific recent moments, and never pitches.

3. III

   Evidence

   Each session ends as a machine-readable report — pushed to your endpoint, and available over the API and CLI.

What it isn't

• Not a survey. It's a conversation that probes — evidence, not Likert-scale clicks.
• Not a panel. It talks to the people you bring — your lost deals, your customers.
• Your hypothesis stays hidden. The agent never reveals what you're testing, so answers aren't contaminated.
• Your data is yours. Structured evidence flows into your stack, not a shared pool.