Predictive maintenance flags the symptom. Kausalyze maps the causal chain through your process, from the upstream variable that is actually driving the failure to the intervention that prevents it.
Process manufacturers have poured money into data infrastructure and predictive tools — yet unplanned-downtime losses keep climbing. The tools got better at flagging that something is wrong. They never learned to explain why. And the veteran engineers who could read those flags are retiring.
Sources: Siemens, The True Cost of Downtime 2024. Rockwell, State of Smart Manufacturing 2026. Per-facility figure is a benchmark-derived estimate for modelling and varies by site.
A process upset does not pause while the team investigates. Finding the true root cause of a recurring problem takes days or weeks. Lost yield, off-spec product, excess energy and avoidable maintenance add up the whole time.
Predictive tools flag that an asset is drifting or that a failure is likely. They do not say which upstream variable caused it or which set-point to move. Most plant AI also cannot show how it reached its conclusion, so engineers cannot check it. To prevent a failure you need the cause, in a form an engineer can check.
A single platform that turns the time-series data you already collect into four things your engineers can act on.
A continuous, system-level view of equipment condition and process stability across the connected unit — not asset-by-asset in isolation, but how the whole system is actually behaving.
A living map of cause and effect for your process.. See which upstream variables are driving a given failure and how they're connected — with the evidence, so your engineers can validate it against what they already know.
Early warnings with time-until-failure estimates and confidence levels — so your team acts on the emerging problem instead of reacting to the breakdown.
Specific interventions to prevent the failure, each with expected impact quantified. Not "this will fail" — but "change this, and here's what it's worth."
Indicated values are published industry figures, not Kausalyze performance claims. Site-level value is established in the pilot, against the plant's own records.
Predictive maintenance tools forecast that an asset will fail. AI co-pilots summarise data and answer questions about it. Kausalyze finds the cause of a failure across the unit, checks it with your engineers and prescribes the change.
| Capability | Predictive maintenance | AI co-pilots | Kausalyze |
|---|---|---|---|
| Explains the cause of a failure | ✕ | ✕Narrates the alert, cannot evidence a cause | ✓ |
| Models the whole unit, across assets | ✕ | ✕ | ✓ |
| Bounded by physics, checked by engineers | ✕ | ✕ | ✓ |
| Prescribes the fix and quantifies it | ✕ | ◑Suggests, cannot quantify or check | ✓ |
| Forecasts failure, with time and confidence | ◑Asset by asset, from the symptom | ✕ | ✓ |
| Runs on existing data. Results in 12 to 16 weeks | ◑Often needs new sensors and asset models | ◑Fast to deploy, nothing to check | ✓ |
| No new sensors or control software | ◑ | ◑ | ✓ |
Predictive maintenance: asset-level anomaly and remaining-useful-life tools. AI co-pilots: chat and summary layers built on general-purpose language models.
Kausalyze is working with Huntsman Polyurethanes' Global Excellence Team on heat-exchanger fouling, building causal models of how operating conditions actually drive fouling and degradation, and validating them jointly with Huntsman's engineers.
Kausalyze gives us deeper insight into asset behaviour than conventional predictive maintenance tools.Process Improvement Global Excellence Team, Huntsman Polyurethanes on the collaboration.
One unit, 12 to 16 weeks, in four phases. You export the data, we build the model, your engineers check it, and you get a value readout for the unit at the end.
We agree the unit and the failure or off-spec problem to target. You export 12 to 24 months of historian data and send it with the tag list and P&IDs.
We build a causal model of the unit from your data. Physics and process constraints are applied so the model cannot propose impossible links.
Your engineers review each proposed cause against what they know about the unit, confirm or reject it, and the model is updated with their answers.
We prescribe set-point changes and maintenance timing with the expected impact of each, and put a value figure on the unit built from your own production and margin numbers.
What we need from you: one unit, 12 to 24 months of exported historian data, P&IDs, and a process engineer for a few hours a week.
See more product detailsEnter your annual downtime exposure per site, the share you believe is avoidable once the causes are known, and the number of sites in scope.
A short technical review is usually enough to tell you — within one conversation — whether we're a fit for your process.
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