How to Automate Lead Qualification With AI

The AI Lead Qualification Architecture

Stage 1: Lead Enrichment

Auto-augment the lead record with company data (industry, size, revenue, tech stack) from APIs like Clearbit, Apollo, or Clay.

Stage 2: Fit Scoring

Use an LLM to score each lead against your Ideal Customer Profile. Inputs: company size, role, industry, use case. Output: fit score (1–100) with reasoning as JSON.

Stage 3: Intent Detection

Analyse free-text inputs for buying intent signals: urgency language, specific feature requests, competitor mentions, timeline indicators.

Stage 4: Routing

High-fit + high-intent → immediate sales rep notification + outreach draft. Medium-fit → nurture sequence. Low-fit → disqualify.

Sample Qualification Prompt

"Score this lead from 0–100 for fit with our ICP: [ICP description]. Lead data: [lead object]. Return JSON: {score, rationale, top_signals, disqualifiers}"

Expected Results

• 40–60% reduction in manual qualification time
• Consistent scoring regardless of who's working
• Faster response to hot leads (minutes vs hours)

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