Core Capabilities

TSI empowers engineers to interrogate and derive inferences from time series data seamlessly.

Data Ingestion

TSI ingests data into a streaming interface, enabling seamless processing and analysis. Data can be ingested from a variety of sources while dealing with data flow challenges resulting from upstream errors, networking glitches and changing operations.

Signal Manager

Organize the signals into flexible hierarchies

Workspaces

Workspaces serve as a developer workbench where users can build, train, test, and manage a variety of AI models within the Falkonry TSI platform. These workspaces can be organized by assets, processes, or operational areas based on the asset hierarchies and trees defined within the system, enabling scalable and structured model development across the enterprise.

1. Model Type: Calculations

Calculations transform raw data into derivative time series, highlighting domain-specific characteristics based on expert knowledge. These transformations, akin to Matlab expressions and Python functions, offer a flexible and intuitive mechanism for applying standard signal derivations.

2. Model Type: Rules

Rules convert analog quantities like raw signals, anomaly scores, and condition labels into discrete events based on thresholds automatically assessed by Falkonry TSI. They enable condition-based actions through a no-code interface, leveraging specific behaviors derived from signal combinations.

3. Model Type: Anomalies

Anomaly models automatically analyze time series data for anomalies by identifying deviations from normal operating behavior without requiring setup or supervision. They help users understand the origins and contributors of anomalies, enabling appropriate actions to mitigate their effects and likelihood.

4. Model Type: Patterns

Patterns uncover meaningful temporal correlations, such as early warnings or stages of deterioration, in complex systems using multivariate data streams. Designed for reliability engineers and data analysts, Patterns facilitate continuous monitoring of specific events and known conditions.

Live Assessments

Run the selected models to analyze time series data in real-time. These models may continuously monitor for anomalies and help perform root cause analysis (RCA).

Reports

Reports visualize time series data through flexible formats such as time plots, value distribution plots, and temporal trends. They provide dynamic access to time series data based on time ranges, enhancing visibility and root cause analysis.