Your automation team likely builds predictive models using custom code or data science tools, which may be limited to a small group of technical experts. Seeq democratizes this capability by:

Allowing domain experts (process engineers, operators) to build models without writing code

Reducing development time from weeks to hours with pre-built functions for signal cleansing, anomaly detection, and forecasting

Integrating seamlessly with Python and machine learning models for hybrid workflows

Providing an audit trail and visual documentation of logic for easier model validation
Seeq complements your existing team and frees up valuable engineering time by enabling a broader group of users to explore and model data independently.

Historians like OSI PI and AVEVA are excellent at storing and retrieving time-series data, but their core function is not analytics. Seeq adds significant value by:

Enabling fast, multi-source analytics without exporting data

Providing context-aware visualizations, time-windowing, and batch comparisons

Allowing users to link process data with metadata like maintenance logs, lab data, and MES systems

Enabling event detection, root cause analysis, and production optimization at scale
In essence, Seeq turns your data historian into a full-fledged industrial analytics platform without duplicating infrastructure.