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Technical guides, customer stories, and product updates
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Speech-To-Text

Should you host an in-house speech-to-text solution or outsource to an API provider?

Businesses across industries are adopting speech-to-text (STT) technology to unlock new use cases and meet growing customer expectations. Whether it’s powering virtual assistants, transcribing conversations, or analyzing audio data for insights, STT has become essential for delivering seamless and engaging experiences.

Speech-To-Text

Best speech-to-text APIs

Whether you’re looking to add voice-based AI into your products to automate customer support, enhance note-taking, supercharge your meetings, or more, this list will help you narrow-in on the right provider for your needs.

Speech-To-Text

Key techniques to improve the accuracy of your LLM app: Prompt engineering vs Fine-tuning vs RAG

Large Language Models (LLMs) are at the forefront of the democratization of AI and they continue to get more advanced. However, LLMs can suffer from performance issues, and produce inaccurate, misleading, or biased information, leading to poor user experience and creating difficulties for product builders.

Speech-To-Text

Keeping LLMs accurate: Your guide to reducing hallucinations

Over the last few years, Large Language Models (LLMs) have become accessible and transformative tools, powering everything from customer support and content generation to complex, industry-specific applications in healthcare, education, and finance.

Case Studies

Transforming note-taking for students with AI transcription

In recent years, fuelled by advancements in LLMs, the numbers of AI note-takers has skyrocketed. These apps are increasingly tailored to meet the unique needs of specific user groups, such as doctors, sales teams and project managers.

Speech-To-Text

RAG for voice platforms: combining the power of LLMs with real-time knowledge

It happens all the time. A user submits a query to a large language model (LLM) and swiftly gets a response that is clear, comprehensive, and obviously incorrect.