Industries
Every heat leaves a record. Almost none of it gets read.
Steel plants instrument everything and analyse a fraction of it. We work on the gap — process models fitted to your own heats, quality predicted before the laboratory confirms it, and reporting that stops being a morning ritual.
Process models · Quality prediction · Energy · Yield · Reporting
01 — Where the time goes
The historian is full. The question is still open.
Data volume was never the constraint here.
Level-1 and level-2 systems produce more signal per heat than anyone reads, and the laboratory result that settles an argument arrives after the material has moved on. What the plant needs is not more measurement — it is a model of what the measurements imply while there is still time to act.
We fit those models to your own process history, alongside the metallurgists who know which correlations are physics and which are an artefact of how a sensor is mounted. That review is not a formality; it is what stops a model learning the plant's habits instead of its chemistry.
The same work usually pays for itself twice — once in quality and once in energy, because both are set by the same decisions and are almost never optimised together.
Talk to Zealogics02 — What you get back
A model of the heat, while the heat is still running.
PROCESS SIGNALS, LABORATORY RESULTS AND YIELD, MODELLED TOGETHER.
03 — What the work covers
Where we work in heavy industry.
Where this shows up
Melt shop, casting and rolling operations
Quality, metallurgy and laboratory functions
Energy and consumable cost programmes
Maintenance and refractory planning
Group reporting across multiple plants
Typical stack
Process optimisation
Setpoint and practice models fitted to historian and level-2 data, reviewed by metallurgy before anything goes near a control room, and delivered as advice rather than as an unattended control loop.
Quality prediction
Predicted properties and defect risk from process conditions, so material is flagged while it can still be diverted instead of after the laboratory confirms what it already is.
Energy and consumption intelligence
Energy, electrode, refractory and consumable use modelled per heat and per shift, with the drivers attributed rather than left as a monthly total to explain.
Plant and compliance reporting
Production, quality and emissions reporting assembled from the source systems, with the arithmetic visible and the exceptions raised rather than buried.
04 — What ships with it
What a heavy industry engagement leaves behind.
A demonstration is not a deliverable. These are the artefacts an engagement leaves with your team — owned by you, runnable without us, and auditable by whoever has to sign for them.
01
A heat-level dataset
Process, laboratory and yield data joined per heat, which is usually the first time anyone has seen them in one table.
02
Quality prediction models
Scored against held-out heats and reviewed by metallurgy, with the features they lean on made explicit.
03
Setpoint advisory
Recommendations into the control room with the reasoning attached, and an operator override that is recorded rather than resented.
04
Energy attribution
Consumption broken down by the decisions that drive it, per heat and per shift, rather than per invoice.
05
Generated plant reporting
The daily and monthly packs assembled from source data, with the manual consolidation step removed.
06
Drift monitoring
The check that tells you a model has stopped describing the process — after a reline, a grade change or a new supplier.
05 — Agentic AI, in this sector
Agentic AI in heavy industry.
Where agents start
- —Heat report drafting
- —Deviation log summaries
- —Specification and grade search
- —Consumable usage summaries
- —Maintenance ticket drafting
- —Shift performance briefs
Where they go next
- —Quality prediction per heat
- —Setpoint advisory agents
- —Root cause analysis across heats
- —Energy optimisation loop
- —Yield loss investigation crew
- —Emissions reporting automation
Delivery note — Models advise; the control room decides. Nothing we build closes a loop on safety-critical equipment without an explicit, separately governed decision.
06 — In your words
What clients say before they call us.
“We find out the material is out of specification from the laboratory.”
In-process quality prediction
“Energy cost is a number we explain, not one we manage.”
Per-heat energy attribution
“Two shifts run the same grade completely differently.”
Setpoint advisory
07 — The rest of the map
Eleven more sectors we work in.
A problem is rarely unique to its industry. Most of what we build here has been built next door as well, which is usually why it arrives faster the second time.
Fab
Semiconductor
Yield investigations, equipment data engineering and the tool intelligence that shortens a root-cause cycle.
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Plant
Manufacturing
Shop-floor copilots, predictive maintenance and the plant reporting that currently happens in a spreadsheet at six in the morning.
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Assets
Energy & utilities
Asset performance, inspection data and the field workflows that still move on paper between three systems.
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TIC
Testing, inspection & certification
Digitised inspection, generated reports and compliance search across standards that change under you.
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Network
Telecom
Network operations assistants, incident response and service assurance analytics that keep up with the alarm volume.
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Finance
Financial services
Reconciliation, close, risk narrative and reporting automation with an approval gate on everything that moves a number.
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Assurance
Audit & assurance
Evidence review copilots, parallel testing agents and document analysis that cites the page it read.
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Clinical
Healthcare
Documentation support, coding assistance and operational insight, with every clinical output approved by a person.
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Public
Government
Citizen services, records intelligence and deployment models that stay inside the boundary the mandate requires.
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Controlled
Defense
Engineering support and controlled AI environments — local models, no external calls, clearance-aware retrieval.
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Talent
Staffing
Engineering, IT and AI talent for Fortune 500 programmes across the USA, UAE, Taiwan and India.
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Have a problem worth solving?
Bring us a grade that gives you trouble.
One grade, one line, your own historian data. That is enough to find out whether a model can see the problem before the laboratory does.