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CASE STUDY

Kardi AI

AI-powered ECG monitoring platform for continuous heart health tracking and early detection of cardiac anomalies.

Industry Healthcare / MedTech / Digital Health

Solution AI-Based Continuous Cardiac Monitoring

Engagement 6 Months

Services AI & Full Stack Development

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Client Context

Kardi AI is a Czech Republic-based MedTech startup developing a continuous heart monitoring solution that combines a wearable ECG chest strap with a mobile application and AI analytics to help individuals and clinicians track heart rhythm and detect arrhythmias early. The system provides medically valid reports and real-time alerts that support preventive care and clinical decision making.

Founded in 2022 and backed by venture investors, Kardi AI has raised funding to enhance its technology and expand internationally, partnering with cardiologists and healthcare professionals to increase access to continuous cardiac monitoring beyond traditional snapshot tools.

Continuous ECG Monitoring

AI-Assisted Arrhythmia Detection

MedTech Innovation & Expansion

The Challenge

Create an AI-enabled ECG monitoring platform capable of ingesting continuous cardiac signal data, handling noise variability from wearable sensors, generating timely alerts for irregular rhythms, and delivering reliable clinical-grade reports that integrate with clinician workflows and support preventive intervention.

Reliable continuous ECG signal processing

Deep learning arrhythmia detection accuracy

Low-latency real-time inference

Medical compliance and reporting

Our Solution

We built an AI-centric cardiac monitoring platform that continuously collects ECG signals from wearable chest straps, processes them through noise-filtering pipelines, applies deep learning arrhythmia detection models, and generates actionable risk alerts and detailed medical reports accessible in real time via mobile and backend systems.

Continuous ECG Data Pipelines

Captured long-term cardiac signals via BLE wearables and standardized them for analysis.

Deep Learning Arrhythmia Models

Trained models to detect arrhythmias with clinically valuable precision.

Real-Time Inference Services

Provided low-latency, cloud-based analytics and alert streams.

Secure Reporting & Clinician Interfaces

Delivered detailed reports for clinicians with secure data storage and compliance frameworks.

How We Delivered

Discovery & Alignment

Analyzed cardiac monitoring needs, clinical workflows, and wearable data constraints.

Architecture & Planning

Designed scalable ECG data ingestion and AI analysis architecture.

Engineering & Integration

Developed signal pipelines, deep models, and real-time services.

Testing & Validation

Validated model accuracy and system performance under real use conditions.

Deployment & Support

Launched production monitoring, tuned models, and provided operational support.

Outcomes Delivered

94% Arrhythmia Detection Accuracy

Deep learning ECG models achieved high clinical alignment in identifying irregular heart rhythms from continuous signal data.

60% Earlier Risk Identification

Continuous monitoring surfaced cardiac anomalies significantly earlier compared to traditional episodic ECG assessments.

<2s Real-Time Alert Latency

Low-latency inference pipelines delivered near-instant anomaly alerts to users and clinicians.

99.9% Platform Uptime

Secure, scalable infrastructure maintained high availability for continuous ECG streaming and reporting.