AI-Powered Lead Generation: From POC to Production

Building an AI-powered lead generation system taught me valuable lessons about taking LLM-based products from proof-of-concept to production. Here's the journey.

The Challenge

Manual lead qualification was eating up 80% of our sales team's time. We needed to automate prospect research, scoring, and initial outreach while maintaining quality.

Multi-Model Architecture

Rather than relying on a single LLM, we built a multi-model system:

• GPT-4: Complex reasoning and content generation
• Claude 3.5 Sonnet: Document analysis and summarization
• Gemini 1.5 Flash: High-throughput classification tasks

This approach gave us automatic failover and cost optimization.

From POC to Production

The transition required addressing several challenges:

1. Rate Limiting: Implementing exponential backoff and queuing
2. Cost Management: Caching, prompt optimization, and model selection
3. Accuracy: Evaluation frameworks and human-in-the-loop validation
4. Monitoring: Tracking token usage, latency, and success rates

Prompt Engineering at Scale

Effective prompt engineering made the difference:

• System prompts with clear role definitions
• Few-shot examples for consistent output
• Structured output with JSON schemas
• Chain-of-thought reasoning for complex tasks

Results

The system now qualifies leads with 95% classification accuracy and reduced manual work by 80%. The key was treating it as a production engineering problem, not just an AI problem.