Ekkono Solutions provides an embedded software development kit (SDK) that enables edge machine learning for Internet of Things (IoT) devices, allowing them to learn from local usage patterns and perform real-time predictive maintenance. This technology enhances product functionality by enabling features such as auto-configuration and condition-based monitoring, ultimately extending product life and improving operational efficiency.
Funding
$6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many Internet of Things (IoT) devices lack the ability to learn and adapt to their specific usage environments after deployment. This limitation prevents them from optimizing performance, predicting maintenance needs, and personalizing functionality based on individual user patterns. Traditional machine learning approaches often require cloud connectivity, which introduces latency, security concerns, and reliance on external infrastructure.
Solution
Ekkono Solutions offers an embedded software development kit (SDK) that enables edge machine learning directly on IoT devices. The SDK allows devices to continuously learn from local data, adapt to changing conditions, and perform real-time predictive maintenance without relying on cloud connectivity. By integrating incremental learning and pipelined data processing, the solution empowers devices to enhance product functionality through features like auto-configuration, condition-based monitoring, and virtual sensors. This approach extends product life, improves operational efficiency, and enables personalized user experiences.
Target Audience
The primary target audience includes IoT device manufacturers and developers seeking to enhance their products with edge machine learning capabilities, particularly in industries such as industrial automation, automotive, and smart home.
Features
- Embedded software library designed for developers to integrate edge machine learning into IoT devices.
- Incremental learning capabilities that allow devices to continuously adapt to new data and changing conditions.
- Integrated pipelined data processing for real-time, continuous learning on the device.
- Toolbox includes change and anomaly detection, signal processing, and data validation with conformal prediction.
- Hot-swappable models with a small footprint and no third-party dependencies.
- Federated learning capabilities for collaborative model training across multiple devices.