SDR, AE, Enterprise

Hiring sales? Here's how we evaluate.

We score how candidates uncover real pain and handle a price challenge — not whether they can recite a discovery script. The model runs the role-play; the rubric is ours.

See the rubric

What we score

The dimensions, not the playbook.

We don't publish the exact criteria, weights, or sub-probes — that's how candidates would game the rubric. Here's what every sales candidate is scored against.

Discovery depth
Whether they uncover the actual business problem behind the stated request, or stop at the first answer. We probe past surface needs and see whether they probe past them too.
Qualification rigor
Did they qualify for fit, urgency, and decision-power — or did they pitch into a vacuum? We score the questions they asked, not just the deal they advanced.
Objection handling
Whether they anchor to value or capitulate on price the moment a prospect pushes back. Composure under pressure is scored separately from technical correctness.
Communication clarity
Whether they can explain the value prop in their prospect's language — not in your marketing's. Jargon and acronym-density get surfaced, not rewarded.

Sample scenarios

What candidates actually face.

Two illustrative scenario types — the actual prompts vary per session and stay private to your tenant.

Scenario 1
Discovery role-play with a synthetic prospect.
The AI plays a buyer with a stated need that masks a different real problem. We score whether the candidate uncovers the real one — and how they navigate when their first hypothesis is wrong.
Scenario 2
Objection handling on a price challenge.
Mid-conversation, the prospect pushes back hard on price. We score whether the candidate anchors back to value, asks the right question to reframe, or folds. Composure is part of the score; volume isn't.

Integrity signals

What we watch for — and what stays private.

We name the signals we capture, but not how we weight or threshold them. That's the part that breaks if we publish it.

  • Every session is recorded — audio, video, and full transcript — and retained per your tenant policy.
  • Every score ships with an ML confidence band. Low-confidence scores are flagged for human review before the candidate is decided on.
  • Response timing is tracked relative to a tenant baseline; suspiciously polished, perfectly-timed answers get surfaced.
  • Admin labeling lets your team flag interviews where the AI's read of the role-play diverged from what a senior closer would catch.
  • We never train shared models on your candidate data.

What we measure

The outcome you can defend.

Discovery-depth score, objection-handling score, and end-to-end completion rate for every candidate — plus a confidence band on each. We measure how often our 'strong hire' candidates clear your second-round panel, and we recalibrate when the gap widens. The metric that matters most: the rate at which our 'no hire' signal earns enough trust to skip the manager screen.

We frame these as what we measure, not as customer-attributed metrics.

Want to see how this rubric scores a real candidate?

An expert will walk you through a live sales interview transcript — including how the integrity signals played out — in 15 minutes.

See pricing
Other domains:EngineeringCustomer Success
SOC 2 Type II — In progressGDPR-readyTenant-isolated infrastructureData residency: USQuarterly bias auditsNo training on your candidate data