Mochi — Voice AI Screening Agent
Draft case study. Content to be expanded.
Summary
Mochi is an intelligent voice screening agent built at Maki People that autonomously conducts phone interviews with candidates in real time.
Context and problem
Screening large candidate pools by phone does not scale with human recruiters.
My role and ownership
Currently leading Mochi. Personally responsible for:
- Real-time audio orchestration framework (LiveKit)
- Multi-agent decision system (finite state machine)
- Turn-management framework (interruptions, end-of-turn detection, flow)
- Self-hosted LLM cluster powering in-call reasoning
Technical implementation
Architecture
- Real-time audio streaming and processing on LiveKit
- Multi-agent decisioning coordinated by a finite state machine
- Turn management for multi-party conversation, interruption handling, and end-of-turn detection
- Self-hosted LLM cluster for low-latency reasoning and dialogue
Stack
- Language: Python
- Real-time: custom frameworks on top of LiveKit
- AI / ML: self-hosted LLM cluster
Key decisions and trade-offs
To be added.
Outcome and evidence
In production at Maki People. Detailed metrics to be added.
Links
- Related: https://www.maki.tech