Eliminate Unplanned Downtime

Proven $5 - 20M EBIT Lift From Mission-Critical Assets in Heavy Industries

2-10% Improvement in Asset Availability and Productivity

Excluding restart penalties, excess fuel and energy use, emissions impact, equipment stress, downstream disruption, and added maintenance loading.

Case Study: Iron & Steel

Estimate Your Lift

Human-on-the-Loop Operations

AI SCORECARD

2,374 Models Deployed

184 Hrs Downtime Saved

74 Helpful Incidents

Human-on-the-Loop

Monitoring Incident Pipeline

Actionable Incidents INTERFACE

Incident #9021 SYSTEM STABLE

Pump Station #2

Confidence: --

Time Series AI

PATTERNS

Agentic AI Reasoning

Did something happen?

Has it happened before?

Where did it all start?

What may happen next?

Domain Knowledge

Physics Rules

Context & Asset Topology

Hydraulic System

Pump Station #2

Time Series Telemetry

Pressure

Speed

Physical Operations

NORMAL

System State: Time Series AI continuously monitoring telemetry streams. No active incidents.

Rapidly, securely and scalably operationalize AI for mission-critical assets

No special sensors, labeling, data science or software engineering required.

Use with any assets and instruementation
Real-time identification of operational incidents
Integrate analytics into standard operating procedures

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Falkonry Wins 2026 AIST Hunt-Kelly Award (AIME)

For

Best Paper, Second-Place

Read the award-winning paper

Use Cases

Root Cause Analysis

Problem:

Frequent and hazardous failure in cold rolling mill causing production loss and requiring root cause analysis involving manual and offline SCADA data review across dozens of parameters

Impact:

$3-$4M per year of lost plant production and wasted operations effort in inefficient recovery.

Solution:

Time Series AI Platform offers an accurate and automated way to find the exact reason behind a failure. It uses the same SCADA data to identify to guide recovery action.

Process reliability

Problem:

Frequent production disruptions due to stoppages of the wet grinding process in the mining plant caused by varying ore quality, which can only be evaluated by offline chemistry laboratories.

Impact:

$30K per hour lost in production and massive clean up and recovery effort to restore the process

Solution:

Time Series AI Platform uses SCADA data to estimate ore grade and give real-time alerts about bad ore entering the process so they can take action quickly to prevent stoppages.

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Production & Yield Improvement

Problem:

Frequent and costly yield losses in fabrication equipment involving corrosive gases resulting from component degradation lead to increased inspection and non-productive time.

Impact:

Reduced yield due to impacted wafers in between inspection and lower production throughput due to non-productive time.

Solution:

Time Series AI Platform provides component behavior warnings days in advance by automatically discovering and recognizing component degradation patterns automatically and non-intrusively.

Discover patterns and anomalies you can't find

Automate the discovery of patterns and anomalies in process and machine data, so you can get higher-level alerts and improve your analytics success and speed.

Rapidly integrate with and operationalize AI/ML

Reduce the time to operationalize AI/ML from months to days with no data engineering, feature engineering or pre-labeling required.

Intuitive, no-code tools for engineers

We democratize the benefits of advanced analytics through intuitive, no-code analysis tools that are easy to use for all mechanical and electrical engineers

Go beyond traditional learning to discover behaviors

Unsupervised learning

Discover every distinct operating state without providing any labels or supervision

Self-supervised learning

Learn the normal range of behaviors from prior history to clearly identify unusual and rare behaviors

Semi-supervised learning

Learn desired mapping between labels and data patterns with as few as 2 examples

Custom models

Apply custom transformations and inference generation based on domain-knowledge