How to Build an AI Lead Enrichment Pipeline

I set up this system to automatically enrich leads with company data, social profiles, and tech stack info. This ai lead enrichment data pipeline automated approach turns manual lead work into automated pipeline growth.

Lead generation is the lifeblood of the business. Automating it means your pipeline fills while you focus on closing.

What You Need Before Starting

• Python 3.8+ with requests
• Anthropic API key
• CRM API (HubSpot, GoHighLevel, or similar)
• Database for lead storage

Step 1: Build the Capture Layer

from flask import Flask, request
import sqlite3
from datetime import datetime

app = Flask(__name__)
db = sqlite3.connect("leads.db")
db.execute('''CREATE TABLE IF NOT EXISTS leads (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    email TEXT UNIQUE, name TEXT, source TEXT,
    score INTEGER DEFAULT 0, created_at TEXT, status TEXT DEFAULT 'new'
)''')

@app.route("/api/lead", methods=["POST"])
def capture():
    data = request.json
    db.execute("INSERT OR IGNORE INTO leads (email, name, source, created_at) VALUES (?,?,?,?)",
        (data["email"], data.get("name", ""), data.get("source", "direct"), datetime.now().isoformat()))
    db.commit()
    return {"status": "captured"}

Step 2: Qualify with AI

import anthropic
from dotenv import load_dotenv

load_dotenv()
client = anthropic.Anthropic()

def qualify_lead(lead):
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=256,
        system="Score this lead 1-100. Consider source, engagement, fit. Classify hot/warm/cold.",
        messages=[{"role": "user", "content": f"Lead: {lead}. Score and classify."}]
    )
    return message.content[0].text

Step 3: Route to Right Sequence

def route_lead(lead, score):
    if score >= 80:
        trigger_hot_sequence(lead)
        notify_sales(lead)
    elif score >= 50:
        trigger_nurture(lead)
    else:
        add_to_long_term(lead)

Step 4: Sync to CRM

import requests

def push_to_crm(lead, api_key):
    return requests.post(
        "https://api.hubapi.com/crm/v3/objects/contacts",
        headers={"Authorization": f"Bearer {api_key}"},
        json={"properties": {"email": lead["email"], "firstname": lead["name"], "lead_score": str(lead["score"])}}
    ).json()

What to Build Next

Add multi-touch attribution to know which channels produce highest-quality leads, not just the most volume.

Related Reading

• Building Automated Data Enrichment Pipelines - framework for deciding what to automate first
• How to Use AI for Data Enrichment - building data infrastructure for AI operations
• The Data Flywheel Explained - building data infrastructure for AI operations