Poor transcription led to low-quality AI summaries. High-fidelity text now ensures accurate insights, lifting satisfaction by 12%.
An AI notetaker platform supporting over 31,000 customers transforms unstructured voice data from calls, lectures, and meetings into searchable digital assets.
The platform's previous transcription provider lacked the precision required for accurate downstream analysis. Because the system feeds text into...
“At Grain, we've been increasingly leveraging Large Language Models (LLMs) to help our customers make better sense of this data through AI identification, flagging, highlighting, clipping, and summarization.”
AI meeting notetaker for automated summaries, transcripts, and CRM synchronization.
AI models for speech-to-text transcription and audio intelligence.
Related implementations across industries and use cases
Inaccurate transcripts rendered notes unusable. Precise, multilingual models now power automated summaries and custom voice agents.
PMs worked nights to draft requirements. Now, streaming AI writes docs live during calls—with zero audio stored.
A custom pipeline struggled with overlapping speech. Replacing it cut maintenance and processes hour-long meetings in seconds.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.