AI Sales Agent: Automated Lead Qualification and Meeting Booking
An AI sales development rep that qualifies inbound leads via chat and email, scores them using BANT criteria, and books meetings directly on reps' calendars.
Client: Confidential SaaS Company (Series A)
Sales dashboard showing an AI conversation with a lead being qualified, a lead scoring panel with BANT criteria checkmarks, and a calendar with AI-booked meetings highlighted.
The Challenge
The company generated 500 inbound leads per month from content marketing and ads. Two SDRs manually emailed each lead, asked qualifying questions (Budget, Authority, Need, Timeline), and booked meetings for account executives. Average response time was 4 hours. By the time an SDR responded, 30% of leads had already booked a demo with a competitor. The SDRs spent 70% of their time on leads that would never qualify, leaving qualified prospects waiting. The VP of Sales needed faster response times and better qualification without hiring more SDRs.
Our Approach
We built an AI agent that monitored the CRM for new leads and immediately engaged via email and website chat. The agent used a conversational approach to BANT qualification: it asked about the prospect's current solution (Need), team size and decision process (Authority), timeline for evaluation (Timeline), and budget range (Budget). Each answer was scored on a 1-5 scale, and leads scoring 15+ out of 20 were automatically offered calendar slots for a meeting with an AE. The agent had personality guidelines: professional but not stiff, curious about the prospect's business, and never pushy. It could handle objections ('I'm just researching'), provide case study links relevant to the prospect's industry, and follow up at optimal intervals (Day 1, Day 3, Day 7). We integrated with HubSpot for CRM data and Google Calendar for meeting scheduling.
What We Built
Delivery Timeline
Day 1-3: CRM Integration
HubSpot API connection, lead data sync, Google Calendar OAuth, meeting slot management.
Day 4-7: AI Agent Core
BANT qualification flow, lead scoring engine, conversation memory, personality tuning.
Day 8-10: Multi-Channel
Email sequence engine, website chat widget, follow-up scheduling (Day 1/3/7).
Day 11-12: Dashboard
Lead pipeline view, scoring breakdown, meeting analytics, qualification accuracy report.
Day 13: Testing
Simulated lead conversations, scoring validation, calendar booking edge cases.
Day 14: Launch
Production deployment, gradual rollout (25% of leads → 100%), SDR training.
Tech Stack
Architecture
frontend
Chat widget (React) and Next.js admin dashboard.
backend
Hono on Railway with HubSpot and Google Calendar API integrations.
auth
API keys for widget. HubSpot OAuth for CRM integration.
data
PostgreSQL for conversations and scores. HubSpot as CRM source of truth.
ai
Claude 3.5 Sonnet with conversation memory and BANT scoring prompts.
Security
rbac
SDR, AE, and Admin roles with different dashboard views.
pii
Lead data stored in HubSpot (their SOC 2 covers it). Local DB stores only conversation logs.
audit
Every AI qualification decision logged with reasoning and scores.
monitoring
Daily qualification accuracy report. Alert on unusual scoring patterns.
The Results
“Our SDRs were spending 70% of their time on leads that would never buy. The AI handles all initial qualification now. Response time went from 4 hours to 90 seconds, and our qualified meeting rate more than doubled.”
Key Takeaways
Speed wins in sales. Responding in 90 seconds instead of 4 hours increased qualification rates because prospects were still in buying mode.
BANT scoring by AI is surprisingly accurate when the conversation is structured right. The AI asks better qualifying questions than most junior SDRs because it never forgets a criterion.
The AI should know when to stop selling. Leads who say 'just researching' get value content, not a meeting push. This builds pipeline for 60-90 day deals.
Deliverables
FAQ
Frequently Asked Questions
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