Unlock Smart Manufacturing with Data and AI

Data silos trap production metrics, machine telemetry, quality records, and supply chain data inside disconnected systems — ERP, MES, SCADA, and IIoT edge devices — that cannot share information. The result: no unified view of performance, reactive maintenance, inability to benchmark across plants, and error-prone manual data consolidation. A centralized manufacturing data warehouse built on Microsoft Azure is the most effective long-term solution.

Manufacturers typically see 20–30% reduction in unplanned downtime, 10–15% reduction in maintenance costs, and improved on-time delivery rates. The ROI depends on asset criticality and current failure rates, but most clients recover implementation costs within 12–18 months.

AI-powered computer vision models are trained on labeled images of defective and non-defective parts specific to your product lines. The models run in real-time on cameras positioned at inspection points on the line, classifying each unit and flagging defects — typically achieving 95%+ accuracy on targeted defect classes.

Yes. We have experience integrating with SAP, Oracle, Microsoft Dynamics, Epicor, Plex, and many MES platforms. Our Azure Data Factory-based pipelines are designed to connect to virtually any structured data source — ERP, MES, historian, SCADA, QMS, or flat files — through standard connectors or custom APIs.

A focused initial phase — connecting key data sources, building core data models, and delivering first dashboards — typically takes 8–14 weeks. Full-scale implementations across multiple plants and data domains take longer, but we follow a phased approach so clients start seeing value early.

Effective predictive maintenance models require historical sensor data (vibration, temperature, pressure, current), maintenance records (work orders, failure logs), and operational context (production schedules, operating conditions). Most manufacturers already collect this data — the challenge is connecting and modeling it correctly.