Engineering at Suki: Building the infrastructure behind AI-powered healthcare
Whitepapers
A clinician talks with a patient. A structured, accurate note appears in the EHR. It feels effortless, and that's the point.
Behind that moment is one of the hardest engineering problems in healthcare technology: real-time speech recognition tuned for clinical language, large language models held to a zero-tolerance standard for error, and integrations with EHR systems that were never designed to work together. All of it has to run reliably, at scale, in hospitals with spotty Wi-Fi.
Our engineering team gives you a peek under the hood in this new whitepaper.
What you'll learn
- How audio becomes a clinical transcript. Why a multi-stage voice pipeline recovers automatically from dropped connections, and how speech models are trained to understand "bee pee" as "BP" and "metformin five hundred" as "metformin 500."
- Why AI is moving onto the device. How on-device wake word detection, voice activity detection, and offline-first design improve reliability and privacy, so no audio leaves the device unless the clinician deliberately calls on Suki.
- How to do more with less. The GPU, TPU, and model-matching strategies that keep large-scale clinical AI efficient without compromising quality.
- How to make one platform work across many EHRs. The abstraction layer that lets the note engine deliver high-quality output without knowing which EHR sits on the other end.
If you're a CMIO, CIO, CTO, clinical informaticist, or other technology leader in the healthcare space, you'll want to read this paper.
