How It Works
Kausalyze provides visibility into your process health, helps identify problems before they happen, and tells you what you can do to prevent them.
Continuous assessment of equipment condition and process stability
Early warnings with time-until-failure estimates and confidence levels
Specific actions to prevent failures, with expected impact quantified
System-level causal understanding of process failures and quality issues
System-level intelligence, not just asset-level predictions
Most tools track individual pieces of equipment in isolation. But your plant doesn't work that way. A problem in the reactor affects the separator, which impacts the compressor, which degrades product quality. Kausalyze sees the whole system - because that's how problems actually propagate.
Knowing something will fail isn't enough. You need to know why it's failing and what to do about it. Kausalyze doesn't just raise alerts - it provides specific recommendations that prevent failures from occurring in the first place.
Blackbox AI doesn't understand that a heat exchanger behaves differently at startup than at steady state. It doesn't know that certain failures only occur under specific ambient conditions. Kausalyze was founded by chemical engineers who've lived these problems. That domain expertise is encoded into everything we build.
Our solution analyses your process data to find the root causes of recurring problems - not just predict when equipment might fail, but explain why it's failing and what to change to prevent it. Think of it as your best engineer's diagnostic ability, automated and running continuously.
Predictive maintenance tells you when something will fail. We tell you why it's failing - and that's the difference between a warning light and an actual solution.
Your existing tools are performing pattern recognition - they might flag that a pump is likely to fail in the next 72 hours based on vibration signatures or temperature trends. But they don't tell your operators what's causing the degradation, which upstream process variables are contributing, or what to actually do about it beyond scheduling a replacement.
That's why despite a decade of investment in predictive maintenance and industrial AI, unplanned downtime losses have risen 65% since 2019. The tools work as designed - they're just designed to be flags for your most experienced engineers to interpret. When those engineers retire, the flags become noise.
Kausalyze takes a fundamentally different approach. Instead of saying "this asset will fail soon," we say "this asset is degrading because of interactions between these upstream variables, and here's the intervention that will prevent the failure."
It's the difference between a check engine light and a mechanic who can explain exactly what's wrong and how to fix it. We're not replacing your predictive maintenance tools - we're giving your team the engineering understanding to actually act on what those tools are telling you.
We work with the time-series data you're already collecting from your historians and control systems - sensor readings, process variables, quality measurements. Most plants are already capturing what we need; it's just not being used to its full potential.
During a project, we typically ask for 12-24 months of historical data for the unit or process area you want to analyse. This gives us enough variation in operating conditions to map the underlying relationships accurately, including seasonal variations in plant performance.
Designed for enterprise environments, Kausalyze securely interfaces with existing client systems as required, ensuring compliance with cyber security and data management standards.
No - and this is fundamental to our approach. Every insight we provide comes with an explanation of the underlying relationships driving it. We show you which variables are influencing the outcome and how they're connected. Your engineers can validate this against their process knowledge, which builds trust and makes adoption far easier than opaque black-box AI systems.
General-purpose AI tools are trained on text from the internet. They're excellent at summarising documents or answering general questions, but they have no understanding of your specific process, your equipment, or the physics and chemistry that govern your operations.
Kausalyze is purpose-built for process manufacturing. Our solution analyses your operational data to understand the actual cause-and-effect relationships in your plant - relationships that are unique to your configuration, your feedstocks, and your operating conditions. No amount of general knowledge can substitute for that.
