Seeq addresses the challenges of continuous and batch process manufacturing analytics by leveraging software and data management innovations.
All Seeq capabilities are accessible via a REST API for industry-specific templates and third-party products.
Seeq Workbench is a browser-based application for investigation and insight from process data. Seeq Workbench enables discovery and visualization of time series data, knowledge capture, and easy to use tool panels for common analytics functions like searching within signals, data cleansing, boundary definitions, and predictive analytics.
Seeq Organizer is a browser-based application to assemble analyses and visualizations into reports, presentations, and meeting agendas as well as read-only web documents. Organizer Topics are dynamic because they tie directly to the underlying data, and are “time relative” so they can be defined by any batch, shift, day, etc.
Seeq Data Lab
Seeq Data Lab is built on Jupyter Notebooks and a Seeq Python library, called SPy, to enable process engineers to expand their Seeq analytics with Python machine learning, graphics, and scheduling libraries, and data scientists to access Seeq functionality for data connectivity, cleansing, modeling, and other Seeq features.
Seeq Server is the application server for Seeq Workbench and Organizer. Seeq Server may be run on premise on a desktop system or server, or to provide additional scalability, reliability, and storage capacity may be deployed in the cloud in either the customer’s tenant or as Seeq SaaS (Software as a Service) on Amazon Web Services (AWS) or Microsoft Azure.
Seeq is highly extensible with data export, a rich scripting environment, access to Python Libraries with Seeq Data Lab, and a REST API. Data export options include Excel, PowerPoint, and any OData client (Tableau, PowerBI, etc.). The Seeq REST API has SDKs for programming in C#, Python, MatLab, and Java, with additional languages coming soon.
Connect data historians, sources, and silos
No matter where your data is or how it’s stored - on premise or in the cloud, in a historian or a SQL database, in data silos or a data lake - Seeq connects to it without duplicating or moving the data. This includes the ability to integrate data from production and business systems. For more information please visit our data connector page.
Seeq may be set up and run on a dedicated server or virtual machine in less than an hour, depending on the tag count. On-premise installations are typically on the same network as a plant or enterprise historian; Seeq may also be deployed in the cloud on Azure or Amazon Web Services, or a mixed environment of on-premise and cloud resources.