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Conversations

Call Intelligence

Scores every sales conversation against a configured rubric, extracts deal-risk and customer signals with the supporting quotes, and writes the evidence back to the CRM.

Problem

Conversations get recorded but reviewed one at a time, so most of what customers say goes unused. Managers coach from a small sample, and risk in an open deal usually surfaces after the quarter closes.

Solution

Every call is scored against the same rubric and mined for pricing objections, competitor mentions, feature requests and churn language. Each finding carries the quote and timestamp it came from.

01 Solution architecture

SOURCES Conversation platform Zoom / Gong / Teams / Chorus Completed recordings and transcripts CRM Salesforce / HubSpot Account, opportunity, owner, stage Calendar Google / Outlook Invite context for matching PROCESSING Eligibility filter Internal and non-sales calls dropped Retry and backfill on failed pulls Account resolution Waterfall: invite → email domain → transcript mentions → LLM inference Rubric scoring Versioned rubric, config hash stored Managers own the criteria Signal extraction Objections, competitors, asks, churn Quote + timestamp on every finding Validation gates Confidence, grounding, dedup, volume cap Failures held back, never written 01 02 03 04 05 DELIVERY CRM write Scorecards and insight rows on the opportunity AI fields separate from human-entered fields BI layer Power BI / Looker / Tableau Counted by distinct call, not row Coaching digest Weekly summary to managers in Slack or email Monthly voice-of-customer roll-up

02 Estimated impact

The value comes from two places: earlier intervention in deals at risk, because risk language surfaces while the deal is still open rather than in the post-mortem, and better rep coaching, because managers coach from every call against one rubric instead of from the handful they had time to listen to.

Estimated additional revenue per year
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How this is calculated
  • A lift of L points on a win rate of W raises closed revenue by L÷W. Going from 22% to 24% is a 9% revenue increase, not a 2% one.
  • The lift figure is yours to set. We make no claim about what it should be — at 0 the model returns nothing.
  • This is a model built from the inputs above, not a measured result at your organisation.
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