Every Setpoint,
Optimized. Live.
Our Through-Process AI connects data across your entire production chain and streams optimal setpoints back to your line — cutting fuel, scrap, and yield variance in a closed loop.
Backed by Vision AI inspection and materials-science ML, MatWhiz Ingenuity brings advanced manufacturers the same optimization playbook the world's leading steel, cement, and chemical plants already run.
The World's Best Plants Already Run on AI
Across steel, cement, chemicals, and refining, closed-loop AI optimization has moved from pilot to production. These are published results from across the industrial AI landscape — and the same playbook we engineer for your line.
Blast & Reheating Furnace Optimization
Steel mills running ML on burden distribution have cut blast-furnace coke rates by ~4% while holding hot-metal quality. On reheating furnaces, AI combustion control typically starts at 3–5% fuel savings and grows to 5–12% as models learn each furnace's thermal behaviour.
Deep-Learning Process Control in Closed Loop
Platforms like Imubit write AI-optimized setpoints directly back to kiln and process-unit controls — stabilising pyro systems, coordinating mills and airflows, and lowering energy per tonne while holding quality targets in real time.
Explainable ML for Production Decisions
Fero Labs' software has surfaced over $20M in savings for manufacturers like Gerdau and CELSA Nordic — cutting 100,000+ tons of CO₂ and close to a million pounds of raw material by letting plant engineers act on explainable model recommendations.
This is no longer experimental. We bring the exact same closed-loop playbook to your plant — engineered around your sensors, your PLCs, and your quality targets.
Where Legacy Systems Fall Short
Heavy industrial operations demand precision, speed, and reliability. Generic IT tools don't survive the heat, vibration, and data volumes of a real production line.
Terabytes of Process Data Sit Idle
Sensors, cameras, and plant control systems generate massive data streams — but without AI-driven correlation, none of it prevents downtime, fuel waste, or yield loss.
Defects Slip Past Manual Inspection
Human-only inspection catches 60–70% of surface defects at best. A single quality escape can cost ₹3–10Cr in claims, rework, and client penalties.
Trial-and-Error Dominates Materials R&D
Developing new alloys requires months of expensive lab synthesis. Mapping compositions to structural properties using AI can compress entire R&D cycles.
Three AI Systems, One Platform
Industrial-grade systems that integrate sensor networks, camera nodes, and predictive algorithms — designed to survive real production environments.
Through-Process AI (TPAI)
We link data across the entire production chain — correlating chemical inputs, operational sensor logs, and final inspection reports to optimize manufacturing variables and minimize scrap.
Our models operate as active Level-2 closed-loop recommendation systems, calculating optimal operational setpoints and feeding them to operators or PLCs every few minutes.
- Through-process yield optimization and automated scrap reduction
- Active thermal zone optimization and fuel consumption modeling
- Root-cause anomaly tracing across multi-sensor plant histories
Vision AI & Insight Analytics
We deploy edge-triggered, camera-based vision systems that perform both real-time defect inspection and downstream analytics. From contaminant detection on incoming feed to predicting alloy additions, we extract process intelligence from visual streams.
Unlike horizontal computer vision platforms, our models adapt to harsh plant floors, integrating with local automation networks, line cameras, and PLC logic.
- Real-time surface anomaly and contaminant detection (99%+ accuracy)
- Preliminary charge addition prediction for melting and casting optimization
- Custom inspection pipelines tailored to automotive, glass, and packing
Materials Science & Property Prediction
Our materials PhDs build ML pipelines mapping chemical composition and heat-treatment history to structural mechanical outcomes — tensile strength, hardness, and corrosion resistivity.
By combining physics-informed modeling with metallurgical databases, we help R&D labs synthesize new alloys and optimize processing windows.
- Composition-property prediction for steels and superalloys
- Processing window mapping to avoid crack formation in casting
- New composition suggestions to substitute expensive raw materials
Anatomy of the Closed Loop
Four stages, running continuously against your live plant data. This is the same architecture behind every headline result in industrial AI — and behind every MatWhiz deployment.
Sense
Live streams from your sensors, historians, cameras, and quality records — synchronized across every production stage, down to 5-second intervals.
Predict
Hybrid digital twins — physics equations fused with deep neural networks — simulate how each stage will respond before anything changes on the floor.
Optimize
The optimization core evaluates thousands of setpoint scenarios in real time, locating the combination that minimizes fuel and scrap within safe limits.
Actuate
Optimal setpoints stream to your operators or directly into PLC registers — with engineering interlocks so every recommendation respects hard safety bounds.
Built to Your Spec, Not a Template
Every plant has unique equipment, data systems, quality targets, and operational constraints. We do not offer off-the-shelf products — every engagement is custom-engineered from the ground up.
Custom Use Case Design
We work with your engineers to identify the highest-value AI application for your specific process — whether that's a vision module for your incoming raw material gate, a predictive model for your batch reactor, or a charge optimization system for your furnace. If it can be AI-powered, we'll design it to fit.
- Contaminant detection for incoming feed streams
- Charge addition prediction for melting & casting
- Custom inspection pipelines for any production line
Works With Your Existing Infrastructure
You don't need to upgrade your plant to work with us. We engineer integrations around what you already have — whether that's a modern DCS, legacy PLCs, manual inspection points, or basic sensor networks. We bridge the gap between your existing systems and AI-driven intelligence.
- Compatible with multiple data providers
- Works alongside manual inspection and operator workflows
- No mandatory factory software system prerequisites
Phased, Low-Risk Engagement
We don't ask for full-plant commitment on day one. Every engagement starts with a focused pilot on a single line, furnace, or inspection point. Results are validated against your own quality benchmarks before any scale-up discussion.
- Pilot-first, measurable ROI before full rollout
- Fixed-scope pilots with defined success criteria
- Scale on your timeline, not ours
From Discovery to Full Deployment
We design and pilot custom setups built around your production topology — with measurable validation at each milestone and your process IP kept strictly confidential.
Site Immersion
We embed directly in your production environment — analyzing layouts, existing equipment, and plant workflows firsthand. No two plants are the same.
Data Assessment
We audit your available operational data — sensor logs, control outputs, quality records — to validate fidelity and define the right scope for your specific line.
Solution Design
We plan camera placements, sensor configurations, compute architecture, and quantitative success markers — fully tailored to your production topology.
Development & Pilot
We build custom ML architectures and deploy a functional pilot on a single production line or vessel for validation before any wider rollout.
Joint Validation
Your plant engineers validate model inferences against real quality data. We iterate with you until agreed accuracy targets are met.
Scale & Handover
We expand across lines, connect to your plant's existing dashboards and operator workflows, document everything, and train your team.
Engineered for Heavy Industry
We replace horizontal AI platforms with deep domain expertise and end-to-end engineering — tailored to your specific processes, infrastructure, and goals.
Domain Depth, Not Just Data Science
Our team includes materials science and metallurgical PhDs who understand dislocation physics and phase kinetics — not just mathematical curve-fitting.
Tailored to Your Infrastructure
Whether you run legacy PLCs, modern DCS panels, or bare sensor networks — we adapt our integration layer to work with what you have, not what we wish you had.
End-to-End Delivery
From hardware selection and camera mounting to inference software and operator dashboards — we own every layer so you have a single accountable partner.
Rapid Pilots, Measurable ROI
We scope engagements so you see measurable results in weeks — defect catch rates, fuel savings, or yield improvement — before committing to full-scale rollout.
Trusted by Industry Leaders
The Team Behind the AI
A specialized deep-tech company founded by engineers and scientists with backgrounds in materials science, AI/ML, and industrial software — built to serve manufacturers globally.
Questions Plant Teams Actually Ask
What data do we need to have before starting?
Usually less than you think. If your plant logs temperatures, pressures, chemistry, or quality outcomes anywhere — a historian, a DCS, SQL databases, or even structured spreadsheets — we can start. Our first step is always a data assessment that tells you exactly what's usable before you commit to anything.
Do we need to install new sensors or upgrade our PLCs?
No. We engineer around your existing infrastructure — modern DCS, legacy PLCs, or basic sensor networks. Where a specific use case genuinely benefits from extra instrumentation (e.g. a camera node for inspection), we scope, supply, and install it as part of the engagement.
How long does a pilot take, and what does it cost?
A typical pilot runs 8–16 weeks on a single line, furnace, or inspection point, with fixed scope and quantitative success criteria agreed upfront. You validate the results against your own quality benchmarks before any scale-up discussion — so the ROI is proven before the rollout decision.
Is our process data and IP safe?
Yes. Every engagement starts with an NDA. Your process data stays under your control — we support on-premise deployment, and models trained on your line are never reused for other clients. Process IP confidentiality is contractual, not a promise.
How are optimization recommendations delivered to the line?
Two modes: advisory (recommended setpoints streamed to operator dashboards every few minutes) or closed-loop (written directly to PLC registers within hard engineering interlocks you define). Most clients start advisory and switch on closed-loop after validation.
We're not a steel plant. Does this still apply?
The through-process approach applies to any multi-stage production chain — cement, glass, chemicals, batteries, automotive machining, food processing. If your process has setpoints, sensors, and a quality outcome, it can be optimized.