By

Jon Stojan

Published

November 14, 2025

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Busy clinics and virtual visits don’t exactly make it easy to take notes manually. That’s the tech gap Shunyalabs.ai set out to target with ZeroMed: the AI-driven speech recognition system designed for hospitals, telehealth platforms, and ambient-scribe setups.

The release focuses on speed, accuracy, and privacy-minded deployment, allowing teams to transcribe spoken conversations into text with fewer issues. Used well, the tool may help organizations streamline documentation and reduce routine retyping across everyday workflows.

What ZeroMed is designed to do

ZeroMed was built to convert spoken words into text for professional healthcare environments. The system is positioned for live dictation, ambient capture during conversations, and recorded audio transcription. It’s meant to handle overlapping speakers and varied room conditions, then return time-aligned text that can be reviewed and edited.

With the program, Shunyalabs.ai aims to make speech-to-text conversion fast and easy to start, simple to monitor, and straightforward to export into existing systems.

Reported accuracy and performance

Shunyalabs.ai reports that internal testing reveals a word error rate of 11.1% and a character error rate of 5.1%. Those figures describe how often recognized words or characters differ from the spoken audio.

The company frames ZeroMed as suitable for both real-time and batch processing, noting that its approach is designed for low latency while maintaining consistent output. In practice, this could mean capturing spoken notes during a session or processing recordings afterward with similar results.

Deployment choices and privacy posture

Many organizations work under strict data-handling rules. To address this reality, ZeroMed can run entirely on-premises, including CPU-only environments, allowing teams to keep their data within their own infrastructure.

Shunyalabs.ai states that this option is designed to help customers meet privacy and security requirements, including common healthcare and data-protection regulations. For groups that prefer cloud or hybrid approaches, ZeroMed can be configured to accommodate these models as well. It’s meant to be flexible without locking teams into a single path.

Training efficiency and ongoing updates

The company emphasizes efficiency on the model-training side. According to Shunyalabs.ai, ZeroMed reaches full convergence in roughly three days using two A100 GPUs and a relatively small set of real-world audio.

The footprint may lower the cost of updates and allow more frequent refreshes as terminology changes. Put simply, the vendor is prioritizing a short feedback loop so new phrasing, accents, and workflow patterns can be incorporated without long delays between releases.

Where teams might use it

ZeroMed is positioned for ambient scribing, live dictation during professional encounters, telemedicine transcription, and similar documented tasks. Speaker diarization separates voices, allowing a transcript to show who said what, even when people talk over each other.

Meanwhile, outputs are structured to support downstream search and analytics workflows, which may help teams review interactions, annotate segments, or route text into internal tools. While outcomes vary by organization, fewer manual steps could free up time for higher-value work.

How to get started

Shunyalabs.ai offers pilot evaluations and early-access integrations, allowing teams to test the fit before a wider rollout. The first release supports English, with additional language options planned for future releases. Because every environment differs, most organizations will still want a review process with human oversight, especially during initial deployment.

Even so, the combination of on-premises options, reported accuracy figures, and flexible training may provide operations leaders with a practical entry point to modern speech-to-text technology. Shunyalabs.ai positions ZeroMed as a focused utility rather than a sweeping promise. If it works as described, it could cut steps between talking and typed text. That could be particularly helpful during long days with back-to-back sessions.

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In this article:AI, Healthcare, Tech

Written ByJon Stojan

Jon Stojan is a professional writer based in Wisconsin. He guides editorial teams consisting of writers across the US to help them become more skilled and diverse writers. In his free time he enjoys spending time with his wife and children.