Retell AI
YCombinator W24 • Voice AI Infrastructure
Role: Software Engineering Intern
Duration: May 2025 – Aug 2025
The Problem
Retell AI, a YCombinator-backed startup, was experiencing rapid growth but faced critical scalability challenges. With enterprise clients demanding reliable voice AI integrations across multiple CRM platforms, the existing infrastructure couldn't handle the volume of 30,000+ daily API calls while maintaining sub-second response times.
Challenge
The primary challenge was three-fold: First, building robust integrations with 8+ enterprise platforms (Salesforce, HubSpot, Zendesk) while maintaining consistent performance. Second, scaling the FastAPI backend to handle exponentially growing call volumes without degrading user experience. Third, creating a streamlined demo process that would enable the sales team to showcase integrations quickly during client meetings.
Solution
I engineered a comprehensive solution involving three key components: (1) A multi-threaded FastAPI backend with Redis queueing that distributed load efficiently across workers, (2) An abstracted integration framework that reduced development time for new CRM connections from weeks to days, and (3) Google AppsScript-powered demo environments that allowed sales teams to showcase integrations in real-time during client calls.
Process
- Research & Discovery (Week 1-2): Analyzed existing API bottlenecks and interviewed sales team about demo requirements. Studied enterprise client integration patterns across different CRM platforms.
- Architecture Design (Week 3-4): Designed multi-threaded FastAPI architecture with Redis queueing system. Created abstracted integration framework to reduce code duplication across platforms.
- Development & Testing (Week 5-10): Built 8+ enterprise integrations with comprehensive error handling and monitoring. Implemented performance optimizations and load testing protocols.
- Deployment & Optimization (Week 11-12): Deployed to production with zero downtime migration. Monitored performance metrics and iteratively optimized based on real-world usage patterns.
Challenges Overcome
- API Rate Limiting: Implemented intelligent backoff strategies and request batching to work within each platform's rate limits while maintaining throughput
- Data Consistency: Created atomic transaction patterns with rollback capabilities to ensure data integrity across multiple CRM systems
- Real-time Demo Reliability: Built sandbox environments with pre-populated test data that could be instantly deployed for client demonstrations
Impact & Results
- Daily API Calls: ~5,000 → 30,000+ (500% increase in capacity)
- Integration Development Time: 2-3 weeks per platform → 3-5 days per platform (80% faster development)
- Client Satisfaction: N/A → 96% demo success rate (Measurable sales impact)
Key Achievements
- Developed 8+ integrations (Salesforce, Hubspot, Zendesk, GHL, etc.) serving ~40 enterprise clients
- Improved systems-level performance enabling 500% increase in daily API call capacity
- Established abstracted integration framework reducing development time by 80%
- Created reliable demo infrastructure with 96% success rate for sales presentations
Tech Stack
FastAPI, Python, Redis, Google AppsScript, Salesforce API, HubSpot API, Zendesk API, GoHighLevel, Multi-threading, Queue Management