Speech-To-Text
Despite rapid advances in voice agent stacks, including speech-to-text (STT), text-to-speech (TTS), and large language models (LLMs), the real-world promise of fully autonomous voice assistants remains largely unmet.
.png)
Speech-To-Text
Voice AI has taken a leap in 2025. From early IVR experiments that barely handled a “hello” to today’s real-time conversational agents running across industries, we've come a long way. But is it truly a solved issue?
Speech-To-Text
As voice agents become a core part of customer and employee experience, users need to know these AI systems are accurate, safe, and acting within boundaries. That’s especially true for enterprise-grade tools, where a rogue voice agent can severely damage relationships and create major legal risks.
Case Studies
The contact center is transforming. Traditionally defined by manual workflows, siloed data, and reactive customer service, today's Contact Center as a Service (CCaaS) platforms are embracing a new era—one driven by real-time AI and automation.
Speech-To-Text
Latency can make or break a voice experience. Whether you’re building an agent that must stop speaking the moment a customer interrupts, or you’re captioning live content, you need a clear, reproducible way to measure how fast your STT really is, from first partial word to final transcript.
Speech-To-Text
Great customer support experiences rely on clear communication and deep understanding. Until recently, meeting that expectation at scale was nearly impossible—human agents can only handle so many languages, and even fewer can switch between them fluently.
