Zuma builds agentic AI systems for multifamily property management, operating at scale across thousands of apartment communities serving millions of residents. The system handles lead engagement, tour scheduling, and rent collections - repetitive operations work that creates bottlenecks for onsite teams - while maintaining human oversight for relationship-critical interactions. The architecture is designed for human-AI collaboration rather than full automation: AI agents process high-volume, structured tasks while property managers handle hospitality and community engagement where judgment and relational context matter.
The technical approach emphasizes rapid iteration driven by field feedback from property managers. Engineers and designers work directly with operations teams to identify latency and reliability requirements in production environments - tour scheduling conflicts, communication failure modes during collections, lead response time sensitivity. This operational integration surfaces real constraints: property management workflows involve variable tenant needs, time-sensitive coordination, and edge cases where escalation to human judgment is the correct trade-off. The system is designed to amplify existing teams by removing operational overhead rather than replacing domain expertise.
Venture-backed by Andreessen Horowitz and Y Combinator, headquartered in Santa Monica. The company ships product rapidly, prioritizing deployment feedback over extended development cycles. Technical domains span agentic AI implementation, human-AI collaboration interfaces, and operations integration - work that requires understanding both inference system design and the operational complexity of residential property management at scale.