The reliable spine
The non-AI spine is the source-linked workflow: clean records, rules, calculations, integrations, exception queues, approvals and reporting for Energy and melt-cost-per-tonne monitoring.
Metals, Foundries & Steel
Energy and melt-cost-per-tonne monitoring
Energy is the largest controllable cost in a metals plant and almost nobody watches it daily. The cupola, induction furnace, reheat furnace or heat-treat furnace burns coke, petcoke, gas or power, and the true cost per tonne, melted or rolled, is reconstructed at month-end from a power bill and a coke ledger, by which time a drifting furnace, a slipping power factor, a wrong charge mix or a fuel-price spike has already cost lakhs. The owner knows the plant is energy-heavy but cannot see, today, which furnace, shift or grade is bleeding. ? reported: petcoke costs rose sharply on import curbs and better raw-material utilisation has cut melt costs materially (reported figures, not verified) precisely because the cost is large and the lever is real. Problems are found at month-end when they are already expensive: rejection creeping up on one pattern or grade, melt or burning-loss yield slipping, energy per tonne drifting, scrap rate moving against a buying position, a customer's payment slowing. Nobody can watch every number every day.
Who has it
Sharpest for foundries (scrap, yield and melt energy), re-rolling mills (power factor, burning loss and rate per tonne) and forging and heat-treatment units (yield, die life and cost), and lighter but real for fabrication shops and for scrap traders watching the rate spread.
What we build
A layer over furnace and power-meter readings, the charge sheet, the fuel ledger and the production record that computes energy-and-melt cost per tonne by furnace, shift and grade, every shift, and flags when it drifts outside its normal band: a power-factor slip, a coke-rate spike, a charge mix costing more than it should, a yield drop that lifts the per-good-tonne cost. The owner sees the number daily; the furnace stays the operator's to run. Early-warning signals on the connected numbers: a figure moving outside its normal band gets flagged and pushed before it becomes a loss, with thresholds tuned per furnace, grade and process.
What is automated, where AI helps, who signs off
Automation for the routine. A person on every decision that matters.
Where AI helps
AI is limited to bounded reading, extraction, matching, clustering or drafting from the firm's own data for Energy and melt-cost-per-tonne monitoring; it never owns the number, the approval, the promise or the decision.
Who signs off
A named person signs off anything touching money, stock, a customer promise, a regulated filing, a payment, a price, a credit decision or a people decision.
What changes day to day
The plant shifts from a month-end energy post-mortem to a same-shift warning; a drifting cupola or a slipping power factor is caught in days, not at the next bill; the charge mix is costed before it is melted. Illustrative: an induction-furnace foundry put true cost-per-tonne on one screen by furnace and shift, caught a recurring night-shift power-factor and charge-mix drift, and brought its energy-and-melt cost down by a few percent of a number that is most of its cost. Illustrative; final numbers come from your own data.
Illustrative outcome
The plant shifts from a month-end post-mortem to a same-shift warning; a drifting cupola, a slipping power factor, a creeping rejection pattern or a yield slip is caught while it is still cheap to fix, and the charge mix is costed before it is melted. Illustrative: an induction-furnace foundry put true cost-per-tonne on one screen by furnace and shift, caught a recurring night-shift power-factor and charge-mix drift, and brought its energy-and-melt cost down by a few percent of a number that is most of its cost. Illustrative; final numbers come from your own data.
Illustrative; final numbers come from your own data.
Path to the build
How this one gets built.
Book a free 60-minute call, then a free Blueprint on the firm's own records. Deep-dive and build, followed by run and govern so the workflow keeps paying back.
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