Voice Agent

Multilingual voice agents become expensive quickly, especially when every live conversation holds realtime speech and model capacity. Concurrency limits can become a product constraint before the experience is ready to scale.

I'm exploring whether the voice pipeline can be broken into more efficient pieces so each conversation costs less while the system supports more simultaneous sessions.

The project experiments with Azure speech and language models, measuring where latency, model usage, and session limits accumulate across a multilingual conversation.

The goal is a practical architecture that routes each part of the voice loop deliberately instead of paying the highest realtime cost for every stage. The implementation is still under construction, so results will be documented as they become repeatable.

• An evolving Azure-based pipeline for multilingual voice conversations.
• Cost and concurrency experiments across the realtime voice loop.
• A project structure ready to document benchmarks and trade-offs as the build matures.

• Still under construction — no public demo or repository yet.
• The current focus is proving a lower-cost path to multilingual voice at higher concurrency.