A voice agent that answers in real time — and answers correctly.
Document-grounded retrieval, automated evaluation, and event-driven call orchestration for a production voice agent used by B2B real-estate teams.
Xujing “James” Mao — engineer
I’m a CS undergrad at Georgia Tech, moving from Sydney. I most recently built real-time voice AI at Voqo, and I’m drawn to problems where latency, correctness and scale all have to be true at once — and I like proving it with numbers.
01 — About
I grew up in Sydney and cut my teeth on olympiad problems — the kind where a clever bound or the right data structure is the difference between an answer and a timeout. That instinct never left. Today it shows up as retrieval that stays off the latency path, schemas that skip migrations, and tuning loops that finish in a third of the time.
At Voqo I worked across the stack of a live voice agent: the retrieval that grounds it, the evals that keep it honest, and the call orchestration that hands a caller to a human without dropping the line. I care about the boring guarantees — idempotency, precision, reconnection — because that’s what “real-time” actually costs.
02 — Selected work
03 — Stack
Recognition
04 — Contact
I’m looking for internships where correctness and latency both matter. If that’s you, the quickest path is a 15-minute call — or just email me.