Custom manufacturing AI & data engineering services

We engineer data integration layers that connect ERP (SAP, NetSuite, Epicor), MES (Plex, DELMIAworks), and legacy SCADA systems through standardized APIs and industrial protocols. Our platforms implement event-driven architectures using Apache Kafka for real-time data streaming, enabling centralized monitoring dashboards that aggregate production metrics, quality data, and equipment status across previously siloed systems.

We build sensor data collection pipelines that continuously monitor vibration, temperature, pressure, and performance metrics from production equipment. Our ML models analyze historical failure patterns and real-time sensor streams to predict equipment degradation 2-4 weeks before critical failures, enabling scheduled maintenance during planned downtime rather than emergency production stoppages.

We develop automated inspection systems using industrial cameras and deep learning models trained on your specific defect types—surface scratches, dimensional variations, color inconsistencies, assembly errors. Our platforms integrate directly into production lines, providing millisecond-level defect detection with automated part rejection and quality analytics that identify root causes upstream in the manufacturing process.

We implement edge computing infrastructure that extracts data from PLCs and CNC controllers using Modbus RTU, OPC-UA, Profinet, and proprietary protocols. Our gateway solutions translate legacy industrial protocols into modern REST APIs and MQTT streams, enabling cloud analytics platforms to access decades-old equipment data without replacing functional machinery or disrupting production operations.

We create ETL pipelines that continuously extract production data, transform metrics according to manufacturing standards (OEE, cycle time, yield rates, downtime categories), and load results into interactive dashboards. Our systems eliminate manual spreadsheet consolidation by automatically generating shift reports, daily production summaries, and executive-level analytics with drill-down capabilities to machine and operator level.

We build integration frameworks connecting procurement systems, supplier APIs, logistics providers, and production schedules into unified supply chain dashboards. Our platforms implement demand forecasting models that analyze historical consumption patterns, production plans, and supplier lead times to generate automated reorder recommendations and alert procurement teams to potential material shortages before they impact production.

We implement data governance systems that enforce standardized measurement units, validate sensor readings against expected ranges, and flag calibration drift in real-time. Our platforms include automated data cleansing pipelines that handle missing values, outlier detection, and cross-system reconciliation, ensuring analytics models train on reliable datasets that produce actionable manufacturing insights.

We architect manufacturing data platforms using containerized microservices (Docker, Kubernetes) that enable incremental capability expansion from pilot programs to enterprise deployment. Our systems implement API-first designs with standardized data models, allowing new production lines, facilities, or use cases to onboard rapidly without rebuilding integration logic or disrupting existing operations.