Semarize
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Use caseRevOps

Revenue intelligence
that's actually structured

CRM data is only as good as what reps enter. Semarize extracts structured fields from every conversation - budget, timeline, stakeholders, risk - and feeds them directly into your systems.

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The problem

CRM data
doesn't reflect reality

Revenue operations depends on accurate data. But the most important signals live in conversations, not CRM fields.

CRM data is incomplete

Reps update fields inconsistently. Budget, timeline, and stakeholder data is often missing or stale.

Forecasts are based on opinion

Forecast calls rely on rep confidence, not structured signals from actual conversations.

Sales stages don't reflect reality

A deal in "Proposal" may not have discussed pricing. Stage progression doesn't match what happened on the call.

Process adherence is invisible

You can't measure whether MEDDICC is being followed when the only data is self-reported CRM activity.

Why existing tools fail

Existing tools
analyse CRM data that's already wrong

Forecasting and pipeline tools layer analytics on top of CRM data - but if the underlying data is incomplete, the insights are unreliable.

Forecasting platforms

Analyse CRM activity data. If reps didn't update the CRM, these tools are working with incomplete inputs.

Conversation intelligence platforms

Surface insights in dashboards - but they don't write structured fields back into your CRM or warehouse. The insights stay in their UI.

Manual pipeline reviews

Managers review deals one by one. Doesn't scale. Bias toward deals with recent activity. Misses early risk signals.

The Semarize approach

Semarize turns conversations
into CRM-ready fields

Extract structured signals from every call and push them directly into CRM fields, BI dashboards, and automation workflows.

Automatic CRM enrichment

Extract budget status, timeline, decision makers, and competitors from conversations. Write them directly to opportunity fields.

Forecast risk signals

Detect pricing hesitation, competitor mentions, legal blockers, and missing next steps. Flag at-risk deals before they slip.

Process adherence measurement

Score MEDDICC, BANT, or your custom framework per deal. See exactly which criteria were covered and which were missed.

Pipeline-grade data

Boolean flags, numeric scores, and extracted values - not narrative summaries. Data your BI tools can query, trend, and model.

Bricks & Kits

Example Bricks for
revops

These Bricks evaluate the specific dimensions that matter for revenue operations leaders. Bundle them into Kits to create reusable evaluation frameworks.

budget_confirmed
boolean

Explicit budget confirmation or commitment mentioned

→ false
timeline_mentioned
string_list

Timing signals and urgency cues detected

→ ["Q2 2026"]
decision_maker_present
boolean

Economic buyer was on the call

→ true
competitor_mentioned
extracted

Competitor names and context identified

→ "Gusto"
procurement_mentioned
boolean

Procurement process or legal review referenced

→ true
pricing_hesitation
boolean

Pushback or hesitation after pricing discussion

→ true

Forecast Risk Kit

kit

Surface deal risk signals from every conversation.

budget_confirmedboolean
timeline_mentionedstring_list
competitor_mentionedextracted
procurement_mentionedboolean
pricing_hesitationboolean
next_step_confirmedboolean

Deal Hygiene Kit

kit

Ensure every deal meets minimum data standards.

decision_maker_presentboolean
budget_confirmedboolean
timeline_mentionedboolean
success_criteria_definedboolean

Output

Structured signals,
not summaries

Every evaluation returns deterministic JSON with typed values, reasons, and evidence spans. Same schema every time.

Forecast risk evaluation
{
  "run_id": "run_def456",
  "status": "succeeded",
  "output": {
    "bricks": {
      "budget_confirmed": {
        "value": false,
        "confidence": 0.93,
        "reason": "Budget not explicitly confirmed",
        "evidence": ["...we don't have budget until Q3..."]
      },
      "competitor_mentioned": {
        "value": "Gusto",
        "confidence": 0.90,
        "reason": "Competitor mentioned in pricing context",
        "evidence": ["...comparing us against Gusto on price..."]
      },
      "procurement_mentioned": {
        "value": true,
        "confidence": 0.87,
        "reason": "Procurement process referenced",
        "evidence": ["...legal will need to review the MSA..."]
      }
    }
  }
}

Make your pipeline
data-driven.

Extract structured fields from every conversation. Enrich CRM, detect risk, and forecast with real signals.

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