eHealth Sensor Network PaaS
Time-series data analytics platform for motif discovery.
| Date | Investors | Amount | Round |
|---|---|---|---|
| - | investor investor | €0.0 | round |
| N/A | €0.0 | round | |
| N/A | €0.0 | round | |
N/A | Seed | ||
| Total Funding | 000k | ||
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Trendalyze offers a platform-as-a-service designed for time-series data analysis, enabling users to discover, monitor, and monetize data patterns without extensive statistical modeling. Founded in 2014 by Radoslav Kotorov and Dave Watson, the company aims to empower business professionals, engineers, and scientists to extract insights from large datasets. Co-founder Dr. Rado Kotorov holds a PhD in decision and game theory and has a background in various executive roles focusing on innovation and analytics.
The core of the Trendalyze platform is its Google-like search capability for identifying micro-trends, anomalies, and recurring patterns—or motifs—in time series data. This functionality allows users to pinpoint valuable signals for predictive maintenance, customer churn, financial fraud, and other critical business events. The platform is designed for self-service, reducing the reliance on data scientists by providing intuitive visualization and analysis tools. It can be deployed on the cloud (Azure, Amazon, Oracle) or on-premises and integrates with IoT platforms and technologies like Spark and TensorFlow for machine learning.
Trendalyze serves a wide range of industries, including healthcare, retail, energy, banking, and manufacturing. Its business model is subscription-based, with pricing tiers such as 'Motif Explorer' for individual business users and 'Motif Intelligence' for site-wide real-time data monitoring and prediction. The company has received funding, including a seed round of $100K in April 2017 from Newark Venture Partners. Trendalyze has also partnered with academic institutions like University College London and has been awarded grants for projects in IoT climate data analytics and remote patient monitoring.
Keywords: time-series data, motif discovery, pattern recognition, big data analytics, IoT analytics, predictive maintenance, anomaly detection, data visualization, self-service analytics, machine learning, operational intelligence, sensor data, financial analytics, healthcare analytics, retail analytics, fraud detection, churn analysis, streaming data, pattern search, business intelligence