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Building a Custom Fit Score System: AI-Powered Account Qualification
Building a Custom Fit Score System: AI-Powered Account Qualification
Build a sophisticated account fit scoring system using Clay and Attio. Create modular research agents for B2B focus, headcount, location, and VC status—each outputting normalized scores that combine into a weighted final fit score.
Clay

What You'll Learn

Design a modular, customizable fit scoring system that automatically evaluates accounts against your ICP criteria. Use research agents paired with assessment agents to generate normalized scores across multiple dimensions, then combine with custom weightings.

Why This Matters

  • ICP Precision: Go beyond basic firmographics to nuanced fit assessment
  • Modular Design: Add, remove, or adjust scoring criteria without rebuilding
  • Weighted Scoring: Customize importance of each factor for your business

Key Steps Covered

  • Webhook Trigger – Receive new accounts from Attio into Clay
  • Research Agents – ClayGent research for B2B focus, headcount, location, VC status
  • Assessment Agents – Score each dimension on 0-10 scale with rationale
  • Weighted Calculation – Combine scores with custom weights, apply competitor filter

Tools & Integrations

  • Clay: Research and assessment agents with ClayGent
  • Attio: CRM for account storage and fit score display
  • Enrichment APIs: Company data augmentation

Common Questions

Q: Why separate research agents from assessment agents? A: Research agents collect information without bias toward scoring. Assessment agents then evaluate that research against specific criteria. This separation improves accuracy and makes prompts easier to iterate.

Q: How do you handle scoring consistency? A: Every module outputs a score from 0-10, making them interchangeable. The final calculation applies weights and a binary competitor multiplier (0 or 1). Consistent output schema enables true modularity.

Build This for Us

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