Manufacturing
AI that predicts breakdowns, catches defects and schedules production lines that run themselves
You get predictive maintenance agents, computer vision quality inspection, production scheduling optimisation, digital twins, supply chain visibility, and energy management workflows. All running on your cloud, connected to your MES and SCADA systems, with the audit trails your operations teams require.
Built on your AWS, Azure or GCP tenant. Integrated with your MES, SCADA, and ERP.
Proof of concept
To measurable ROI
AWS, Azure or GCP
Your unplanned downtime costs millions. Your defect rates eat margins. AI fixes both.
Manufacturers lose an estimated 5 to 20 percent of production capacity to unplanned downtime. Quality defects cost 15 to 20 percent of revenue in rework, scrap, and warranty claims. Production schedules still rely on spreadsheets and tribal knowledge. These are not equipment problems. They are decision-speed problems.
We build AI agents that sit between your sensor data and your production decisions — predicting failures before they happen, inspecting quality at line speed, optimising schedules continuously, and giving you a digital twin of your entire production environment.
reduction in unplanned downtime with predictive maintenance AI (McKinsey, 2024)
defect detection accuracy with computer vision at line speed (BCG, 2024)
OEE improvement through AI-driven production scheduling (Deloitte, 2024)
annual value at stake from AI in manufacturing globally (McKinsey Global Institute, 2024)
From sensor data to production intelligence in one governed workflow
We connect your MES, SCADA, PLM, ERP systems, and IoT sensor networks to AI agents that predict failures, inspect quality, optimise schedules, and generate the operational reports your plant managers and quality teams require
MES, SCADA, PLM, ERP, IoT sensors
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Maintenance alerts
- Quality reports
- OEE dashboards
What we deliver for manufacturing
From predictive maintenance to digital twins. Governed, auditable, production-ready.
Predictive maintenance
Detect degradation patterns and schedule maintenance early.
ROI
Process
Ingest sensor streams
Vibration, temperature, pressure, current, and acoustic emission data streamed from equipment sensors into the prediction engine
Detect degradation patterns
ML models identify subtle anomaly signatures across multiple sensor channels that indicate early-stage component degradation
Schedule and alert
Maintenance work orders generated with predicted failure windows, part requirements, and estimated production impact
Sources
McKinsey, 'AI-driven predictive maintenance in manufacturing,' 2024; Deloitte, 'Smart factory analytics,' 2024 — reproduced for illustrative purposes
Quality inspection AI
Inspect every product at line speed for defects and deviations.
ROI
Process
Capture and calibrate
High-resolution cameras and lighting arrays positioned at inspection points, calibrated against known-good reference samples
Classify and score
Deep learning models classify defect types, severity, and root cause indicators at line speed with sub-millimetre accuracy
Reject and trace
Defective units automatically diverted. Defect data fed back to process control for real-time root cause analysis
Sources
BCG, 'Computer vision in manufacturing quality,' 2024; McKinsey, 'AI-powered quality management,' 2024 — reproduced for illustrative purposes
Production scheduling optimisation
Balance capacity, materials, and deadlines in one schedule.
ROI
Process
Model constraints
Machine capabilities, tooling availability, material lead times, energy tariffs, and customer delivery dates mapped into the scheduling engine
Optimise schedules
AI evaluates millions of scheduling permutations, optimising for OEE, on-time delivery, and energy cost simultaneously
Execute and adapt
Schedules pushed to MES with real-time re-optimisation as conditions change — machine breakdowns, rush orders, material delays
Sources
Deloitte, 'AI-driven production planning,' 2024; McKinsey, 'The smart factory at scale,' 2024 — reproduced for illustrative purposes
Digital twins
Simulate process changes before touching physical equipment.
ROI
Process
Build the twin
Physics-based and data-driven models of your production processes calibrated against real sensor data and historical performance
Simulate scenarios
Test process changes, new product introductions, and capacity scenarios in the digital environment before physical implementation
Optimise continuously
Twin runs in parallel with production, identifying optimisation opportunities and predicting outcomes of proposed changes
Sources
McKinsey, 'Digital twins in manufacturing,' 2024; BCG, 'The factory of the future,' 2024 — reproduced for illustrative purposes
Supply chain visibility
Predict supply disruptions and recommend alternative sources.
ROI
Process
Map the supply network
Tier 1, Tier 2, and critical Tier 3 suppliers mapped with lead times, risk profiles, and alternative sourcing options
Predict disruptions
ML models monitor geopolitical events, weather, shipping data, and supplier financial health to flag risks before they materialise
Recommend and act
Alternative sourcing strategies, safety stock adjustments, and production schedule modifications automatically generated
Sources
McKinsey, 'Resilient supply chains through AI,' 2024; Deloitte, 'Supply chain visibility platforms,' 2024 — reproduced for illustrative purposes
Energy optimisation
Cut energy cost per unit through smart scheduling.
ROI
Process
Profile energy consumption
Machine-level, line-level, and facility-level energy consumption profiled against production output and environmental conditions
Optimise usage patterns
AI schedules energy-intensive processes during off-peak tariff windows and optimises HVAC based on occupancy and production heat loads
Monitor and report
Real-time energy dashboards with carbon footprint tracking, regulatory compliance reporting, and continuous improvement recommendations
Sources
Deloitte, 'AI for industrial energy management,' 2024; BCG, 'Decarbonising manufacturing with AI,' 2024 — reproduced for illustrative purposes
CEO, Siemens AG
Roland Busch
"We have deployed AI-driven predictive maintenance across our gas turbine fleet, reducing unplanned downtime by over 20 percent. The models process thousands of sensor data points per second and detect anomalies weeks before conventional monitoring systems flag them. This is measurable operational improvement, not a technology demonstration."
Originally published: Siemens — AI in Manufacturing — reproduced for illustrative purposes
How we work in manufacturing environments
Manufacturing AI must integrate with your existing MES, SCADA, and PLM systems — not replace them. We start by identifying the one production bottleneck or quality issue that costs you the most, then build the AI workflow that eliminates it with measurable OEE, quality, or throughput improvements.
Everything runs on your cloud tenant or on-premise infrastructure. Your production data, your sensor streams, your process parameters. Nothing leaves your perimeter. Your plant managers and quality teams can inspect every decision the system makes.
01
Production and data mapping
We assess your manufacturing data landscape, sensor coverage, and production bottlenecks. Then identify the highest-value use case that delivers measurable OEE or quality improvement.
02
30-day proof of concept
A working AI agent on your cloud, connected to your MES or SCADA system, producing actionable predictions in 30 days.
03
OT/IT integration
Your operational technology and information technology systems bridged through governed APIs with real-time data flows, edge computing where needed, and human-in-the-loop controls.
04
Scale in 12-18 weeks
From one validated workflow to measurable operational ROI across predictive maintenance, quality inspection, scheduling, or energy management.
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