At Weya AI, we work with our own components and multiple STT partners to build reliable, production-grade speech systems. Within this broader stack, Deepgram plays an important role in enabling accurate and fast audio-to-text processing for several of our workflows.​

Speech-to-text is central to many of our internal and customer-facing use cases, where transcription quality, latency, and operational stability matter more than surface-level features.

How Did we Use Deepgram

We evaluate multiple STT solutions including our own and other providers. Deepgram has proven to be especially effective for several of our speech-to-text workflows. Its multilingual STT models deliver strong accuracy and speed, and they have performed reliably in production.

What We're Building Together

Deepgram has been stable and dependable in real-world production usage with minimal ongoing maintenance. The key areas we're focused on are endpointing in noise, semantic segmentation, and background robustness. On our end, we're building custom solutions to handle these, but we're also actively collaborating with Deepgram to drive improvements at the platform level.

Broader language support is another priority expanding coverage for Indian and global languages like Telugu, Kannada, and Bahasa would make it significantly more useful for our global use cases.

Learn more about speech-to-text at deepgram