Soham Ray

Soham Ray is a Research Engineer at Sierra, focused on building conversational AI agents. He has over four years of experience in the customer service AI space, conducting applied research on augmenting and automating task-oriented dialogue systems. He holds a Master’s degree in Computer Science from Cornell University, where he specialized in AI and NLP.

Soham Ray による投稿

White text "Transcription ensembling" on a grainy green and blue split background.

Voice AI is only as good as what it hears

We’ve built a transcription platform that dynamically routes across providers, incorporates a customer’s context, supports 70+ languages, and adapts to real-world variability, improving both agent effectiveness and customer satisfaction.

2026年5月18日

White text "τ-voice" on a blurry, grainy background transitioning from orange to purple.

𝜏-voice: benchmarking real-time voice agents on real-world tasks

𝜏-voice is a benchmark for real-time voice agents on 278 grounded customer-service tasks across retail, airline, and telecom. It pairs deterministic, end-to-end task scoring with realistic, controllable audio — diverse personas, environmental noise, and free-form turn-taking.

2026年5月1日

The text "μ-Bench" on a background of purple sky and green grass.

μ-Bench: an open multilingual transcription benchmark

μ-Bench is an open multilingual transcription benchmark built from customer service phone calls across five locales. It aims to measure transcription providers on the errors that matter in production: not just how closely transcripts match their reference, but whether they preserve the speaker’s intent.

2026年4月20日

The text "τ³-Bench" on a blurry, grainy background of green and brown.

𝜏³-Bench: Advancing agent benchmarking to knowledge and voice

𝜏³-Bench is here. We've expanded agent evaluation to two new frontiers: knowledge retrieval and voice.

2026年3月18日

Gradient mixed colors, voice and global icons

Multilingual voice: building agents that speak to everyone

Multilingual voice agents aren’t just a feature — they’re a bridge to better, more inclusive customer experiences.

2025年10月8日

𝜏²-bench: benchmarking agents in collaborative real-world scenarios

𝜏²-bench challenges AI agents not just to reason and act, but to coordinate, guide, and assist a user in achieving a shared objective. This leap from solo operation to co-ownership of a task pushes agents into a much more demanding space. And, critically, it reflects the kinds of tasks AI agents are increasingly being asked to perform in the real world.

2025年6月10日

Soham Ray

Soham Ray による投稿

Voice AI is only as good as what it hears

𝜏-voice: benchmarking real-time voice agents on real-world tasks

μ-Bench: an open multilingual transcription benchmark

𝜏³-Bench: Advancing agent benchmarking to knowledge and voice

Multilingual voice: building agents that speak to everyone

𝜏²-bench: benchmarking agents in collaborative real-world scenarios

Sierraにできること

Soham Ray による投稿

Voice AI is only as good as what it hears

𝜏-voice: benchmarking real-time voice agents on real-world tasks

μ-Bench: an open multilingual transcription benchmark

𝜏³-Bench: Advancing agent benchmarking to knowledge and voice

Multilingual voice: building agents that speak to everyone

𝜏²-bench: benchmarking agents in collaborative real-world scenarios