01 — Context
Modern manufacturing plants generate terabytes of data every day — from sensors, PLCs, ERP systems, quality checks, and logistics. Most of it goes unanalysed. Equipment failures are discovered after the fact. Quality defects are found at the end of the line. Supply chain disruptions arrive as surprises. We build the data and AI infrastructure that makes these problems visible before they become costly.
02 — The Challenge
From Reactive to Predictive
The difference between a plant that runs and a plant that performs is data. Most manufacturers know their OEE — few know why it fluctuates. Most know when equipment failed — few know it was going to fail three days in advance. Quality defects are caught at inspection — not predicted from the sensor data that preceded them. The data to answer all of these questions already exists. It is just not connected, analysed, or acted on.
03 — Our Approach
We ingest MQTT and SCADA data streams into scalable data lakehouses. Time-series models identify anomalous patterns in sensor data that correlate with upcoming failures. Computer vision models inspect visual output for quality defects in real time. OEE dashboards give plant managers live visibility into performance drivers. ERP integration links operational data to production schedules, inventory, and financials.
Engineering Approach
Built from IoT sensor ingestion pipelines, time-series data stores, asset history databases, edge-to-cloud architectures, and ERP integration layers.
04 — Business Impact
What Changes
Predict equipment failures days in advance, preventing unplanned downtime
Detect quality defects at the source, not at final inspection
Live OEE dashboards replacing end-of-shift manual reporting
05 — Domain Capabilities
What We Build
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