SeeQ

With Seeq, customers will achieve better, faster insights on their data, take action on insights more quickly and improve their production and business metrics

Founded in 2013, Seeq publishes software applications for process manufacturing organizations to rapidly find and share data insights. Oil & gas, pharmaceutical, specialty chemical, utility, renewable energy and numerous other vertical industries rely on Seeq to improve production outcomes, including yield, margins, quality, and safety. Headquartered in Seattle, Seeq is a privately held virtual company with employees and partners in the United States, Asia, Canada, Europe, and South America.

Seeq® is an advanced analytics solution for process manufacturing data that enables organizations to rapidly investigate and share insights from data in historians, IIoT platforms, and database web services—as well as contextual data in manufacturing and business systems. Seeq’s extensive support for time series data and its inherent challenges enables organizations to derive more value from data already collected by accelerating analytics, publishing, and decision making. With diagnostic, monitoring, and predictive analytics powered by innovations in big data and machine learning technologies, Seeq’s advanced analytics solutions help organizations turn data into insights to drive process improvement and increase profitability.

Seeq and OSIsoft

Seeq advanced analytics enables OSIsoft PI system customers to rapidly find, share, and act on insights to get even more value from their data. Seeq is advanced analytics for the OSIsoft PI system including Asset Framework, Event Frames, PI Notifications, Data Archive, and other features. Seeq combines advanced trending and data visualization with Google-like search, data cleansing, and the ability to quickly create and manipulate data objects that represent batches, machine states, or other time periods of interest. In addition, Seeq is browser-based so it can be accessed from any workstation, with results easily shared in real time with colleagues in any location.

Finally, Seeq connects to data in any source, including SQL Server and production systems like MES, Batch, LIMs, EAM and others for contextualization of sensor data in the historian.

Workbench is Seeq’s application for engineers engaged in diagnostic, descriptive, and predictive analytics with process manufacturing data. It includes features to expedite the full arc of the analytics process, from connecting to historians to data cleansing, visualization, modeling, and calculations. Workbench also enables organizations to leverage the work of engineers with features for real-time collaboration, knowledge capture of analytics processes for easy reuse, and the sharing.

Organizer is Seeq’s application for engineers and managers to assemble and distribute Seeq analyses as reports, dashboards, and web pages. Organizer “Topics” may include text, images, scorecard items, and analyses generated in Seeq Workbench such as trending displays, scatter plots, bar charts, etc. Employees across the organization can leverage insights created in Seeq with a “read only” view of the information and when Organizer documents are viewed in a web page (as a URL).

Data Lab is Seeq’s application for data scientists and process engineers to access Python libraries to expand their analytics.Using Seeq Data Lab process engineers can expand their Seeq analytics efforts to the rich ecosystem of Python libraries and data scientists can participate directly in industrial analytics by leveraging Seeq for data access, cleansing, modeling, and other features. Seeq Data Lab is built on Jupyter Notebooks and a Seeq Python library, called SPy.

Find what you Seeq

Seeq® is founded on the premise that many process manufacturing organizations are DRIP “Data Rich, Information Poor” (DRIP) and the number will increase with new sensor deployments and higher data creation rates driven by the Industrial Internet of Things (IIoT). As a result, the existing need for solutions for process manufacturing companies to derive insight from their data will only become more widespread and important in the future.

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