-
Optimize for Industry Jargon
Feed the model a specific list of technical terms, proper nouns, or acronyms before you start. This prevents the AI from guessing phonetically and ensures accuracy for niche fields like healthcare, law, or cybersecurity. It is a simple step that drastically reduces the need for manual post-processing.
-
Select the Model Based on Latency
Use the live-transcribe model for real-time captions or interactive voice agents where speed is critical. Switch to the batch model for processing meetings or podcasts to maximize throughput and minimize processing overhead. Choosing the right tool for the specific timing requirements of your app will save on both cost and compute resources.
-
Simplify Multilingual Workflows
Stop building separate logic or routing for different languages. The model automatically detects and switches between 57 languages mid-session, making it ideal for bilingual environments or international customer calls. This allows your backend to remain lean while still supporting a global user base.
Why it matters
For small businesses, manual transcript cleanup is a massive time sink. High-accuracy transcription that understands your specific industry vocabulary allows you to automate summaries and data entry without constant human oversight. This helps builders ship voice-enabled features that actually work in noisy, real-world conditions.