Tech Stack
Automated lead qualification system that processes inbound emails, extracts budget and timeline from natural language, and routes qualified prospects to calendar booking. Built with Python, Claude AI, and Supabase to eliminate manual lead processing and ensure consistent, fast responses.
Role: Full-stack AI Builder
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Tools: Python, Supabase, Claude AI, Gmail API
Client Problem
Sales teams often waste hours manually filtering spam and low-quality leads.
Solution Overview
Built an autonomous AI agent using Claude API that parses emails, scores lead quality, and drafts replies.
Key Features
Automated Email Responses
System processes leads and generates responses automatically
Qualification Criteria Extraction
AI extracts budget, timeline, decision authority, and project scope from email content
Conversation Context Preservation
Full email thread history maintained for context-aware responses
Automatic Meeting Booking
Qualified leads are automatically routed to calendar booking
Automated Lead Processing
Handles leads automatically outside business hours
Multi-tenant Data Isolation
Each client's lead data and conversations are isolated
Tech Stack
Backend
PythonGmail API
Python webhook handlers processing incoming emails
AI/ML
Claude AI
Claude AI for natural language understanding and response generation
Infrastructure
Supabase
Supabase PostgreSQL storing conversation history, lead data, and qualification signals
Expected Impact (Modeled Benchmarks)
99% Faster Response Time
Automated qualification reduces response time from ~24h to under 2 minutes.
3–5× Conversion Lift
Instant AI follow-ups eliminate SDR delays and increase conversions.
70–85% Lower CPL
Automation reduces reliance on manual SDR teams and drives down CPL.
40–60% Shorter Cycles
Automated sequences, scoring, and routing accelerate qualification cycles.
80–90% Less Manual Work
Automated intake, Q&A, follow-ups, and booking eliminate manual tasks.
Benchmarks are modeled estimates based on industry-standard performance ranges for AI-driven qualification systems. Not client results.
Technical Implementation
- •Webhook handlers process incoming lead emails automatically
- •Claude AI extracts budget, timeline, and decision authority from email content
- •Full email thread history maintained for context-aware responses
- •Qualified leads routed to calendar booking automatically
- •Error handling and retry mechanisms ensure reliability
- •Gmail API used for authenticated email sending from client domain
Screenshots
Main dashboard showing key features and navigation
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Lessons Learned
AutoLeadCloser demonstrates that AI can extract structured qualification data from natural language email content.
The system shows how webhook-based processing enables automated lead handling without manual intervention.
The architecture prioritizes reliability through error handling and retry logic, designed to prevent data loss during processing failures.