🚧 Coming Soon1 Platforms IncludedIntermediate🤖 4 Agents30-60 min

Lead Scoring & Qualification Pipeline

AI-powered lead scoring system that enriches, qualifies, and prioritizes inbound leads using multi-agent analysis of firmographic and behavioral data.

Sales & Marketing

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Includes implementations for CrewAI. One purchase — all platforms.

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  • All 1 platform implementations
  • Full source code & documentation
  • Commercial license included
  • 30-day money-back guarantee
  • Free updates for 1 year
  • 30-day email support

Choose Your Platform

One purchase includes all 1 implementations. Deploy on whichever framework fits your stack.

🤖

CrewAI

Python~30 minutes

CrewAI crew with 4 specialized agents and production-ready tools.

Included in CrewAI version

  • crew.py with 4 agents
  • Custom tools
  • Config templates
  • Deployment guide

⚡ Why OpenClaw?

One-click install, automatic orchestration, built-in cron scheduling, and memory integration. Other platforms require manual setup — OpenClaw gets you to production in minutes.

Code Preview — CrewAI

main.py
from crewai import Agent, Crew

enricher = Agent(role='Lead Data Enricher', goal='Enrich leads with firmographic data', tools=[apollo_api, clearbit])
fit_analyzer = Agent(role='ICP Fit Analyst', goal='Score lead fit against ICP', tools=[icp_scorer])
scoring_crew = Crew(agents=[enricher, fit_analyzer, intent_scorer, router], process=Process.sequential)
🤖
CrewAI
~30 minutes

Agent Architecture

How the 4 agents work together

Input

Your data, triggers, or requests

Agent 1

Data Enricher

Contact & Company Data Enrichment

Enriches raw lead data with company size, industry, tech stack, funding, and contact details.

Apollo APIClearbit LookupLinkedIn Scraper
Agent 2

Fit Analyzer

ICP Scoring & Analysis

Scores each lead against your Ideal Customer Profile criteria.

ICP ScorerIndustry ClassifierTech Stack Matcher
Agent 3

Intent Scorer

Behavioral Intent Analysis

Analyzes behavioral signals — website visits, content downloads, email opens.

Activity TrackerEngagement ScorerIntent Detector
Agent 4

Priority Router

Lead Routing & Assignment

Routes qualified leads to the right sales rep based on territory and capacity.

Territory MapperCapacity BalancerCRM Updater
Output

Structured results, reports, and actions

What's Included

Everything you get with this template

4 platform implementations
4 configured agents
Documentation
Deployment guide
😤

The Problem

Inbound leads pile up and SDRs waste hours researching leads that never convert. Without systematic scoring, hot leads go cold while reps chase unqualified prospects.

The Solution

A 4-agent pipeline that automatically enriches lead data, scores against your ICP, analyzes behavioral intent, and routes qualified leads to the right rep — in seconds instead of hours.

Tools You'll Need

Everything required to build this 4-agent system — click any tool for details

CrewAIRequiredFree

Agent orchestration for the scoring pipeline

Together AIRequiredPay-per-token

LLM provider for analysis and scoring

Apollo.ioRequiredFreemium

Lead and company data enrichment

ClearbitOptionalPaid

Additional firmographic enrichment

HubSpotOptionalFreemium + paid tiers

CRM integration for lead routing

SupabaseOptionalFreemium

Scoring history and analytics database

LangSmithOptionalFreemium

Agent tracing and monitoring

Implementation Guide

8 steps to build this system • 2-3 hours estimated

Intermediate2-3 hours

📋 Prerequisites

Python 3.10+LLM API keyApollo.io or Clearbit APICRM access
1

Define your ICP scoring rubric

Map the 8-12 factors that define your best customers with weights for each factor.

2

Set up lead enrichment connectors

Wire Apollo.io and/or Clearbit APIs for automatic data enrichment.

3

Build the ICP scoring engine

Implement scoring that evaluates enriched data against your rubric with threshold levels.

📘 Complete Blueprint

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Code Preview

Sample agent setup — see platform-specific previews above

Preview only
main.py
from crewai import Agent, Crew

enricher = Agent(role='Lead Data Enricher', goal='Enrich leads with firmographic data', tools=[apollo_api, clearbit])
fit_analyzer = Agent(role='ICP Fit Analyst', goal='Score lead fit against ICP', tools=[icp_scorer])
scoring_crew = Crew(agents=[enricher, fit_analyzer, intent_scorer, router], process=Process.sequential)

Example Input & Output

See what goes in and what comes out

Input
{"lead": {"name": "Sarah Chen", "email": "sarah@techcorp.io", "company": "TechCorp"}}
Output
{"score": 0.87, "fit": "strong", "intent": "high", "route": "enterprise-team"}

Requirements

🐍
Python 3.10+
⚙️
LLM API key

Reviews

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Frequently Asked Questions

Do I get all platform implementations?+

Yes — one purchase includes all platform implementations.

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