# Electrokare: ECG-based physiology models for clinical and performance use

Canonical: https://mudpie.ai/companies/electrokare/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Electrokare: ECG-based physiology models for clinical and performance use](https://mudpie.ai/companies/electrokare/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

Electrokare is building a physiology model that reads the body through signals it already produces, starting with the ECG. Its intended buyers are clinicians, health-system partners and performance teams that want more useful measurements from continuous or existing cardiac signals—not consumers looking for a diagnosis from a landing page.

## What it does

The [current Electrokare site](https://www.electrokare.com/) describes Nefesh as an ECG foundation model and presents two product arms: Electrokare Clinical and Electrokare Performance. It says the platform is trained and validated on more than 3M patients and actively deployed to 40K-plus patients. The site links to a clinical evidence pathway and says the model is intended for precision, preventative medicine and human performance.

The product thesis is to infer changes that are difficult to measure non-invasively today, including cardiac function, valve changes or physiological stress. Those are company-described capabilities and product direction, not a clinical conclusion about any individual. The public pages do not publish a complete regulatory status, reimbursement model or price.

## Why I’d look closer

The advantage is reusing a familiar signal instead of asking people to adopt another invasive test or wearable. The buyer could be a clinical partner, device company, health system or performance program that can define a safe decision boundary around the model output.

The tradeoff is medical validation. A model trained on millions of patients can still perform differently across devices, populations, care settings and disease prevalence. If a result changes care, the clinician needs the evidence, calibration, false-positive cost and escalation path—not only a precise-looking number.

## What I’d ask

Which measurements are clinically validated, in which populations and on which ECG hardware? What is the intended-use and regulatory status of Clinical versus Performance? Can a clinician inspect confidence, provenance and drift? How are patient data, consent, retention and model updates handled? What happens when the signal is noisy or outside the training distribution?

## My editorial take

Shortlist Electrokare for a clinical-evidence or performance-partnership conversation if ECG data is already available and the use case has a clear human decision-maker. Treat the model as decision support until regulatory and clinical evidence say otherwise. The product’s opportunity is making a low-friction signal more informative; its burden is proving that the extra information is safe to act on.

## Quick facts

| Field | Sourced detail |
| --- | --- |
| Product | ECG foundation model with clinical and performance applications |
| Buyer | Clinicians, health systems, medical-device and human-performance partners |
| Company-reported scale | 3M+ patients trained/validated; 40K+ actively deployed |
| Pricing/status | Pricing and complete regulatory status not published in checked pages |
| Main question | Which model output is validated enough to influence a real clinical or performance decision? |

## Sources checked

| Source | Checked |
| --- | --- |
| [Electrokare Speedrun profile](https://speedrun.a16z.com/companies/electrokare) | 2026-09-19 |
| [Electrokare homepage](https://www.electrokare.com/) | 2026-09-19 |
| [Electrokare model page](https://www.electrokare.com/model) | 2026-09-19 |


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.