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Pipeline

Pipeline Generator

Discovers, enriches and scores accounts against your ideal customer profile, and writes qualified targets back to the CRM with the reasoning attached.

Problem

Reps build target lists by hand, each one repeating research the others already did. The lists are stale within weeks, and nobody can say why a given account made the cut.

Solution

The ICP becomes explicit, testable criteria. Accounts are enriched, scored in code, and written back with a tier, a score and the reasoning a rep can read.

01 Solution architecture

SOURCES ICP config Criteria, weights, tier boundaries Versioned, owner-approved CRM Salesforce / HubSpot Existing accounts and ownership Enrichment Clay / Apollo / ZoomInfo / Clearbit Firmographics, headcount, tech stack Company site Fetch + parse Positioning, segments served PROCESSING Normalisation Fields standardised across providers Source record IDs retained end to end Gap detection Missing data stored as explicit gap Absent is not treated as poor fit Deterministic scoring Score and tier computed in code The model cannot overwrite it Guidance generation LLM writes explanation and angle Grounded in the enrichment record 01 02 03 04 DELIVERY CRM account fields Score, tier, rationale on the record Powers qualified account views Outbound handoff Outreach / Salesloft / Instantly Fit reason travels with the account Review queue Conflicting enrichment values held for a person Accept / reject, audited

02 Estimated impact

The value is coverage: reps work a larger, better-qualified account set instead of the handful they had time to research themselves.

Estimated annual value
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How this is calculated
  • The close rate is yours to set and should be conservative — it spans qualification, meeting and win.
  • Coverage only converts if reps actually work the extra accounts.
  • A model from your inputs, not a measured result at your organisation.
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