Motivation

The core motivation for TSI's AI-native architecture stems from the inherent challenges of operational time series data - volume, variety, velocity.

1. Complexity and Volume

Traditional Business Intelligence (BI) and dashboard approaches are ineffective for the massive, complex volumes of real-time industrial time series data. TSI's stream processing capabilities are built to handle "infinite data sets" efficiently.

2. Unknown Unknowns:

All complex systems exhibit emergent behaviors during their operations, leading to unforeseen risks that traditional heuristics-based alerts cannot detect. AI-native analytics are designed to find these "never-before-seen behaviors" without requiring prior setup or labeled examples.

3. Scalability and Usability

Manual feature engineering and reliance on data scientists are not scalable or commercially viable for the vast number of problems. TSI aims to democratize analytics through no-code solutions for anomaly detection, pattern recognition, and rules, making it accessible to engineers and subject matter experts (SMEs).

4. Intelligence Close to Data

The statement "AI will be native to wherever you have data" highlights the need to put intelligence as close as possible to the time series data itself. TSI's architecture, including its efficient DNN models and suitability for edge deployment, fulfills this by optimizing for scalable AI rather than just data storage.

5. Explainability

Unlike black-box AI, TSI provides quantified explanation of results through explanation scores, confidence scores, and anomaly scores, which are crucial for understanding root causes and building trust with operators and engineers.

6. Data Pathologies

Real-world industrial data often has gaps, irregular sampling, and other inconsistencies. TSI's design explicitly accounts for and handles these data pathologies without requiring laborious data engineering, streamlining the path to actionable insights.