AI-Powered Lead Qualification and Booking System
Shorya Vijayvargiya
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Posted Jul 19, 2025
AI-powered system for Nova Finance that qualifies loan leads, auto-books sales calls, and syncs everything to CRM cutting response time from hours to seconds.
✅ Case Study: AI-Powered Lead Qualification & Auto-Booking System for Nova Finserve
Client: Nova Finserve
Industry: Financial Services — Loan Marketplace
Project Title: Qualify & Auto-Book Sales Calls Without Lifting a Finger
🧩 The Problem
Nova Finserve was scaling their paid ad campaigns and receiving 100–150+ inbound leads per week across loan types (debt consolidation, personal loans, small business). But:
Most leads were unqualified — wasting agent time
High-quality leads were not followed up fast enough
Response delays (even 2–3 hours) led to cold leads and drop-off
The sales team was overwhelmed and spending most of their time manually reviewing leads, chasing wrong-fit contacts, or just trying to stay organized
Nova wanted a solution that would qualify leads instantly, auto-book calls for the right ones, and log everything cleanly — without expanding their team or switching CRMs.
⚙️ The Solution I Built
I designed and implemented a real-time AI-powered qualification and scheduling system using Make.com as the automation engine, paired with OpenAI for logic and enrichment.
🧠 Workflow Breakdown
1. Lead Capture → Webhook Trigger
Leads came in via Typeform (also tested with a custom landing page on Webflow)
Submitted data: name, email, phone, loan type, loan amount, credit score, employment status, location
Typeform sent data to a Make webhook, which triggered the entire pipeline instantly
2. AI Qualification Layer
Data was passed to OpenAI (GPT-4o) with a custom prompt logic that scored each lead based on:
Loan amount threshold (minimum $10,000)
Credit score (must be ≥ 620)
Employment status (must be full-time or self-employed)
GPT response included:
Qualification status (Qualified / Unqualified / Needs review)
Reasoning summary
Suggested next step
Qualification logic was contextual, so borderline cases could be passed to manual review
3. Auto-Scheduling for Qualified Leads
If GPT marked a lead as “Qualified,” the system triggered a Calendly API call
Calendly auto-booked a 15-minute slot with a sales rep based on availability (matching time zone, avoiding double bookings)
The lead received a confirmation email and SMS instantly with the meeting details
4. Slack Alerts for Hot Leads
High-value leads (loan amount > $50,000) triggered a Slack webhook:
Custom alert in the #hot-leads channel with lead name, score, amount, and booking status
Rep could click-to-open the CRM entry and pre-review before the call
5. CRM Integration
All leads (qualified or not) were logged into Airtable, with synced fields:
Name, email, phone
Qualification score
GPT summary
Booking status and link (if booked)
Source tag (Facebook Ads, Google Ads, etc.)
Leads were auto-categorized into pipelines:
Qualified + Booked
Unqualified
Needs Review
6. Fallback Logic
If the AI couldn’t confidently assess a lead, the system added it to “Needs Review” in Airtable
A notification was sent via Slack to the ops team to review it manually
Manual override buttons allowed the team to trigger a Calendly booking with one click from Airtable Interface
🛠️ Tools Used
Make.com – Primary automation engine
OpenAI (GPT-4o) – Lead qualification logic
Typeform + Webflow – Lead capture
Calendly API – Auto-booking calls
Slack API – Instant alerts for hot leads
Airtable – CRM and lead pipeline
Twilio – SMS notifications (optional but tested)
🎯 Results
3× increase in booked calls — same ad spend, smarter lead flow
Response time dropped from hours to seconds
90% of lead processing is now fully automated
40+ hours saved per month across SDRs and operations
Sales agents now only speak to pre-qualified, high-conversion leads
No more back-and-forth scheduling or wasted manual reviews
🧠 Why It Worked
Used GPT for flexible, smart qualification — not just rigid filters
Airtable gave the team visibility without needing a full CRM overhaul
All logic was documented and modular — easy to scale, replicate, or adjust based on new lead types
Built for speed + human fallback: nothing gets stuck, and no good lead gets missed
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