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Industries
Manufacturing
Factory intelligence, engineered into the line.
MES-aware data platforms, predictive quality, and AI-augmented operations — engineered for plants where uptime is the contract and tolerances are non-negotiable.
The case
The data exists.
The intelligence doesn't.
Modern plants generate vast amounts of MES, SCADA, and quality data — most of it never reaches the analytics estate, and the data that does is too stale to act on. Predictive quality, throughput intelligence, and energy optimization stay theoretical because the data spine isn't trustworthy.
We engineer the OT-aware data spine and the AI workloads on top — predictive quality, throughput optimization, and energy intelligence — designed to ship value at the line, not in a corporate dashboard.
How we engineer for Manufacturing
The capabilities
that compound here.
Data Lakehouse & Warehouse
An OT-aware data estate — MES, SCADA, quality, and ERP data — governed, observable, and ready for shop-floor AI.
Advanced AI & Analytics
Predictive quality, throughput optimization, and energy intelligence engineered for the realities of the line.
AI-Powered Automation & API
Integration spines that respect OT realities, work-order intelligence, and process-control augmentation.
SAP Data & Analytics
SAP-anchored manufacturing intelligence — clean, governed, and federated with shop-floor systems.
Outcomes we engineer for
Numbers that move
at the operating level.
+12%
OEE on instrumented lines
−28%
Scrap rate
−15%
Energy per unit produced
−40%
Time-to-quality-incident-detection
Common questions
What buyers
ask first.
Can you work with our MES and SCADA stack?
Yes. We engineer governed gateways from the line to the analytics estate, with strict separation between operational and analytical worlds.
How do you handle plants with very different processes?
Common spine, line-specific models. The data primitives are shared; the AI specializes per line — without rebuilding the spine each time.
Is this OT-safe?
We follow ISA-95 separation, zero-trust at the boundary, and respect that operational technology cannot be a science project. Analytical workloads consume; they do not control unless explicitly engineered to.
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Where the same
playbook applies.
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Let's start at the line
Factory intelligence,
engineered into operations.
Tell us where data, quality, or throughput still operate on instinct. We'll diagnose, frame, and propose a starting point that pays back at the line.