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Seeq

United States
2013
Private
< $10m
51 - 200
Open website
Seeq Corporation develops software and services that accelerate
industrial process analytics on industrial process data.

Seeq's sophisticated yet easy-to-use analysis tools are applicable for
industrial markets like oil refineries, pharmaceuticals, and energy
production.
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Industrial process data is the collection of time-series data, events,
and signals, as well as related contextual data, generated by production
and manufacturing organizations. Due to the high volume, velocity, and
variety of these Data Streams, industrial process data typically
requires extensive manipulation to enable insight and analytics.
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Seeq is a provider of Industrial IoT analytics and modeling technologies, and also active in the chemicals, food and beverage, metals, mining, oil and gas, paper and pulp, pharmaceuticals, and utilities industries.
Technologies
Analytics & Modeling
Big Data Analytics
Functions
Process Manufacturing
Industries
Chemicals
Food & Beverage
Metals
Mining
Oil & Gas
Paper & Pulp
Pharmaceuticals
Utilities
Seeq’s Technology Stack maps Seeq’s participation in the analytics and modeling IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
    Actuators
    Sensors
  • Devices Layer
  • Robots
    Drones
    Wearables
  • Supporting Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
None
Minor
Moderate
Strong
Number of Case Studies1
Heat Exchanger Monitoring and End of Cycle Prediction
Predicting end-of-cycle (EOC) for a heat exchanger due to fouling is a constant challenge faced by refineries. Proactively predicting when a heat exchanger needs to be cleaned enables risk-based maintenance planning and optimization of processing rates, operating costs, and maintenance costs. Before using Seeq, the engineer had to manually combine data entries in a spreadsheet and spend hours/days formatting and filtering the content or removing non-relevant data when necessary (for example when equipment was out-of-service).
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