Visplore provides a plug-and-play visual analytics platform that enables users to integrate, explore, and prepare large time series data from various sources without coding. This tool significantly reduces data analysis time from days to minutes, allowing for rapid identification of insights and improved data quality for decision-making.
Funding
$3.9M 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
Analyzing large time series data from disparate sources is often a time-consuming and complex process, requiring coding expertise and specialized tools. This complexity delays insight discovery and hinders effective data preparation for critical decision-making.
Solution
Visplore offers a plug-and-play visual analytics platform designed for integrating, exploring, and preparing large time series data without the need for coding. The platform enables users to break down data silos by combining historians, databases, and files, allowing for rapid assessment of data quality and identification of key insights. Visplore facilitates interactive data preparation, enabling users to correct outliers, filter irrelevant data, and label patterns on the fly. Interactive data presentations can be created and shared, fostering collaboration and data-driven decision-making across teams.
Target Audience
Visplore is designed for engineers, data scientists, and analysts in industries such as energy, manufacturing, and process control who need to quickly explore and prepare large time series data for decision-making.
Features
- Plug-and-play integration with historians, databases, and various file types
- Interactive data exploration with dynamic visualization methods for large time series data
- On-the-fly data preparation capabilities, including outlier correction, filtering, and pattern labeling
- Interactive report creation and sharing with colleagues and customers
- Explainable AI for searching, segmenting, and monitoring patterns
- Python APIs for adding custom data sources and automating workflows
- Local or on-premise deployment options, eliminating the need to move data to the cloud