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Falkonry Website Full Guide (2026)

Operational AI software providing predictive analytics for industrial use.

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Updated May 26, 2026

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Introduction

Falkonry's web platform provides a browser-first interface for industrial engineers and operations teams to deploy and manage predictive analytics for critical assets and processes. It enables users to ingest time-series data from operational technology (OT) sources, configure machine learning models for anomaly detection and prognostics, and visualize real-time operational insights directly within their web browser, streamlining the path from raw sensor data to actionable intelligence for asset performance management.

Key Features

Core Capabilities

1

Time-series data ingestion wizard (for Historian, SCADA, MQTT)

2

Pattern recognition engine configuration panel

3

Predictive model training and deployment interface

4

Real-time operational health dashboard

Additional Details

1

Root cause analysis visualization tool

2

Remaining Useful Life (RUL) estimation module

3

Model explainability feature importance viewer

4

Alerting rule editor for process deviation

Use Cases

For Developers

OT Data Stream Integration & Validation

Data engineers utilize the web interface to configure secure connections to industrial data sources (e.g., Historians, PLCs via gateways), map sensor tags, and validate data quality for AI model readiness, ensuring robust data pipelines for predictive application

Process Anomaly Detection & Optimization

Reliability engineers employ the platform to detect subtle, multivariate anomalies in complex industrial processes, identify root causes through explainable AI, and refine operational parameters to prevent unplanned downtime and improve overall equipment effectiveness (OEE)

How to Use Falkonry

Connect Industrial Data Source

Navigate to the 'Data Sources' section in the browser, select your Historian or SCADA system type, input connection credentials, and map relevant sensor tags to begin streaming time-series data into the platform

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