Read some of our use cases and explore how our clients are solving their unique problems using our solutions.
A medical device startup headquartered in the San Francisco Bay Area is launching the next generation of wearable devices capable of analyzing vital signs from patients. They need to launch a product that can process time-series data coming from several sensors embedded in their device and extract relevant insights helping patients manage their health in real-time. The data coming from the sensors include temperature, acceleration, electrocardiography (ECG) and photoplethysmogram (PPG) signals. The final product needs to be validated for FDA submission.
Our solution consists of three main components: a suite of digital signal processing algorithms (Villca DSP), a machine learning platform (Villca ML) and a tailored set of data management services (Villca Data Services). The Villca DSP module extract time and frequency domain features for each data stream coming from the sensors using state-of-the-art algorithms. The Villca ML module then uses those features to create machine learning models and extract patient insights such as heart rate, oxygenation level and blood pressure. Villca Integration integrates and deploys this solution into the client infrastructure consisting of a wearable device, a smartphone application and a cloud infrastructure. The deployment is done following best software practices for future FDA submission.
Upon completion of the project, the client is able to have a product allowing them to raise $35M in additional funding.
A major U.S. RFID manufacturer and IoT platform software provider is seeking to increase their market share in the retail market. They want to differentiate themselves by offering their clients a comprehensive solution allowing them to track real-time store information, predict customer behavior and infer actionable insights allowing their clients to increase profit and improve customer satisfaction.
Our solution uses Villca DSP to analyze and extract features from their time-series data streaming from the RFID sensors. The data contains product location and store layout information. Villca ML then combines these features with customer data which is then fed into our machine learning models. The models allow to predict indicators such as conversion rates, common customer paths and product movement patterns. Villca Integration port these models into Apache Spark using its Streaming and MLlib libraries. Then the models are deployed into a third-party cloud service and connected to an IoT ingestion engine using the MQTT protocol. Finally, a big-data visualization platform processes the output and offers our client's customer the ability to optimize store layout, reallocate resources and enhance the customer experience in real-time.
A San Francisco based quant hedge-fund focused on value investing strategies wants to take advantage of filing data released by the U.S. Security and Exchange commission and combine them with their proprietary data streams and algorithms. They want to use fundamental data to extract patterns and identify relationships between companies in the same industry. The goal is to predict company performance indicators which combined with their unique know-how could be translated into a solid investment thesis.
Our solution uses Villca Integration to extract, transform and load all the data stream coming from the SEC in the form of XML flat files. The resulting time-series data is then arranged by type (assets, liabilities, revenues, COGS, etc.) and analyzed using Villca DSP to extract frequency and time domain features such as trend, periodicity and other pattern information. Villca ML then combines these features with proprietary client information and build the models to predict relevant key performance indicators for industry sectors or individual companies. The client then uses this information to develop their investment thesis.