Development of an AI-based lead qualification system (Lead Qualification AI)
Manual lead qualification is a bottleneck: SDRs spend 60% of their time on leads that will never sell. An AI system automates the initial screening, assesses ICP compliance, and passes only high-quality leads to the salesperson.
System architecture
Data Enrichment: Incoming lead (email, company, name) → automatic enrichment from open sources: LinkedIn (Proxycurl API), Clearbit, Apollo, BuiltWith (technologies). We also add: company size, revenue, industry, tech stack, LinkedIn profile.
ICP Scoring: Logistic regression / XGBoost on historical qualified leads (200+ examples required). Features: company size, industry match, tech stack fit, role/seniority, geography. Score 0–100 with explanation (SHAP values).
Behavioral Scoring: Website activity (time on page, sections visited, materials downloaded) via GA4/Segment integration. Combined with ICP score.
Email Intent Analysis: If a lead has written an email, NLP intent classification is: just looking, actively studying, ready for a demo, hot.
Automated Qualification Questions: For borderline cases, the AI bot asks 2–3 qualifying questions via email or chat.
Integration with CRM
Auto-scoring when creating a lead. Routing: score >80 → MQL → Sales queue immediately; 50–80 → nursing sequence; <50 → long-term nursing.
Duration: 5–8 weeks
Collecting and tagging historical data is the longest stage. A well-structured CRM accelerates this process.