Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.
If you’re looking to combine speech recognition (STT) and LLMs for cutting-edge voice apps, look no further! Our ultimate guide is finally here, and it’s filled with valuable strategies and hands-on insights from our work with hundreds of audio-first companies and extensive interviews with experts in AI note-taking, sales enablement and customer support.
What you'll learn:
Speech-To-Text
For modern voice-enabled platforms, regulatory compliance isn't optional. Whether you're building contact center tools, sales enablement software, or AI voice agents, security is a cornerstone of trust, a growing customer expectation, and often a legal requirement.
Speech-To-Text
Every product that depends on voice input lives or dies by its speech-to-text performance. Whether you're enriching CRM data from support calls, powering live captions in meetings, or triggering downstream actions via LLMs, transcription accuracy and speed aren’t just nice-to-haves. They’re essential to product functionality. If your STT engine stalls on latency or mistranscribes a customer’s request, it can break automations, derail user experiences, and create costly manual work downstream.
Speech-To-Text
As the landscape of speech-to-text APIs continues to evolve—with growing demands around latency, language support, and compliance—it’s more important than ever to ensure that your setup aligns with your product’s direction.
