Toma operates a voice AI platform for automotive dealerships, processing over 1,000,000 calls since launching in 2024. The system handles inbound phone operations - service scheduling, call routing, and follow-up automation - with safeguards designed to manage transfer latency and revenue leakage. Core technical challenge: maintaining conversational quality and intent detection accuracy across high-variance dealership scenarios (service appointments, parts inquiries, sales handoffs) while minimizing false transfers and dropped context. The platform implements transfer triggers, clawback mechanisms for mistimed handoffs, and follow-up alerts when human staff doesn't complete actions, addressing the operational complexity of human-AI transition points in production telephony.
Infrastructure runs on AWS with a TypeScript/Next.js frontend, PostgreSQL via Prisma for state management, and tRPC for type-safe API boundaries. The voice AI layer must handle real-time constraints - low-latency speech recognition and synthesis, sub-second intent classification - while managing concurrent call volume and dealership-specific context (inventory, scheduling systems, staff availability). Trade-offs center on model selection for conversational understanding versus inference cost at scale, and the reliability surface area of integrating with legacy dealership management systems. Founded by engineers from Scale AI, Uber, Lyft, and Amazon; backed by Andreessen Horowitz and Y Combinator with $17 million Series A funding.
Deployment spans dealerships across the United States, including Pohanka Automotive Group, SCHOMP, Hudson Automotive Group, and Bergey's. Primary bottlenecks likely involve tuning voice models for domain-specific terminology (vehicle makes, service codes, dealership jargon), managing tail latency in transfer decisions where milliseconds impact customer experience, and evaluating conversational success beyond simple call completion - did the AI correctly capture appointment details, route urgency appropriately, preserve customer satisfaction? The system's value proposition hinges on converting missed calls and staff bottlenecks into captured revenue, which requires high precision on intent classification and low false-negative rates on transfer triggers to avoid revenue loss from mishandled interactions.