See how 63 companies
use AI for audio transcription & summarization

Complex email chains bottlenecked utility agents. AI now summarizes 55k threads monthly, cutting handling time by 87%.

Manual fine-tuning held back deployment. Automated optimization cut inference time 63% and agent routine work by over 50%.

Poor transcription led to low-quality AI summaries. High-fidelity text now ensures accurate insights, lifting satisfaction by 12%.

Space static broke standard tools, requiring manual logs. Custom models now run a real-time check and make archives searchable.

Legacy models caused 41 escalated cases. A 90-day switch to cloud AI restored accuracy and enabled real-time analytics.

Adding transcription required six months and four engineers. Nylas launched in one day with a single developer, saving $300k.

Inaccurate transcripts skewed context. AI models now deliver >90% accuracy, helping managers track sentiment and verify compliance.

Transcripts lacked precision for ranking leads. AI now separates speakers and automatically scrubs credit card data from audio.

Building speech tools threatened product focus. Integrating models handled the backend, freeing engineers to build clinical workflows.