AI Lead Qualification System
Every SDR spends up to 60% of their time on leads that will never buy. Our AI system qualifies leads 3 times faster than manual methods, freeing your team to focus on high-probability deals. We build an AI system for automatic lead qualification: AI scoring, enrichment from external sources, and CRM integration. The AI system analyzes each lead across dozens of features in 2–3 seconds, evaluates ICP fit, and passes only quality traffic to sales. The result — your team focuses on deals with maximum close probability. This typically saves companies $30,000–$60,000 per year in SDR costs alone.
Lead chaos is the typical scenario: an average B2B company receives 200+ leads per month. Of them, 70% are non-target: students, competitors, random inquiries. Losses from non-target leads amount to millions of rubles per year. Manual filtering is impossible — SDRs burn out, best leads are lost. Poor prioritization worsens the situation: managers grab flashy but cold leads, while truly hot leads wait. The AI system evaluates each lead across dozens of features in 2–3 seconds and queues them by priority. Context loss — a lead downloaded a whitepaper, visited the pricing page, wrote in chat — the system consolidates all signals into a single score. Without this, it's blind selling.
How the qualification system works
Architecture includes four layers:
- Data Enrichment: Incoming lead (email, company, name) → automatic enrichment from open sources: LinkedIn (Proxycurl API), Clearbit, Apollo, BuiltWith (technologies). We supplement: company size, revenue, industry, technology stack, LinkedIn profile.
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ICP Scoring
Logistic regression / XGBoost on historical qualified leads (200+ examples). Features: company size, industry match, tech stack fit, role seniority, geography. Score 0–100 with explanation via SHAP.
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Behavioral Scoring
On-site activity (time on page, visited sections, downloaded materials) via GA4 / Segment integration. Combined with ICP score.
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Email Intent Analysis
If the lead writes an email — NLP classification of intent: just looking, actively researching, ready for demo, hot.
Sample cost savings calculation
For an EdTech client (1000+ leads/month) we built a two-tier scoring: rule-based filter and ML model on CatBoost (256 trees, AUC 0.87). Result: lead-to-MQL conversion increased 2.3x, processing time dropped from 12 to 2 minutes per lead. Savings on SDR salary — over $50,000 annually. Development cost for a typical system ranges from $25,000 to $75,000, with payback within 3–6 months.How to prepare training data
- Export from CRM at least 200 leads with final status (MQL/SQL/rejected).
- Enrich each lead via public APIs (Clearbit, LinkedIn) — at least 5 features.
- Label statuses: qualified (1) or not (0). For borderline cases, involve an expert.
- Check class balance — use weighted logistic regression or SMOTE if imbalanced.
How does the AI system integrate with CRM?
Integration via REST API or webhooks. On lead creation, the system automatically runs enrichment and scoring, writing results to a custom field. We support Bitrix24, Salesforce, HubSpot, AmoCRM — customization for any CRM is possible.
Why AI qualification beats manual work?
AI lead qualification is 3x faster and 1.5x more accurate than manual. A typical SDR processes 30–40 leads per day with ~60% accuracy. The AI system processes 1000+ leads with 85–90% accuracy (on historical data). The difference — full transparency: every score can be explained with SHAP values.
Parameter Manual screening AI system Leads per day 30–40 1000+ Accuracy ~60% 85–90% Time per lead 12–15 minutes 2–3 seconds Evaluation transparency subjective SHAP explanation Implementation process and timelines
Stage What we do Duration Analytics CRM audit, labeling 200+ leads, ICP requirements gathering 1–2 weeks Design Feature selection, pipeline design, interface mock-up 1 week Implementation Model development, enrichment, CRM integration 2–4 weeks Testing A/B test on historical data, unit tests for each module 1 week Deployment Production launch, monitoring, retraining based on feedback 1 week Total timeline — 5 to 8 weeks. With a well-structured CRM — up to 4 weeks.
What you get in the end
- Scoring model with documentation (model card, SHAP summary)
- REST API for manual and automatic scoring
- CRM integration (Bitrix24, Salesforce, HubSpot, AmoCRM)
- Metrics dashboard (conversion, score distribution, latency)
- Team training (2 hours) + admin documentation
- 12-month code warranty, free first month of support
Common implementation mistakes and how to avoid them
- Insufficient data — fewer than 200 labeled leads → model overfits. Solution: use transfer learning from public datasets.
- Blind trust in score — AI provides a probabilistic estimate, but final decision is human. We configure thresholds: score > 80 → immediate MQL, 50–80 → nurture, < 50 → archive.
- Ignoring feedback — managers override scoring in CRM but don't say why. We log all overrides and retrain the model quarterly.
Contact us for a project evaluation. We have 5 years of experience implementing AI sales solutions, over 30 successful projects. Get a consultation — we'll assess your CRM, data, and propose system architecture within your budget. Request a commercial proposal — we'll reach out within a day.
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