that reduces downtime, eliminates quality defects, and optimizes production throughput.
We engineer custom solutions for predictive maintenance, computer vision quality inspection, real-time production analytics, and supply chain optimization, built specifically for discrete and process manufacturers.
Leaders trusting our AI solutions:
76%
Of manufacturers cite system integration as their #1 operational challenge
58%
Increase in Overall Equipment Effectiveness (OEE) with real-time analytics
$470K
Average annual savings from custom quality and maintenance solutions
Fragmented systems preventing unified production visibility
Manufacturing operations generate data across disconnected ERP, MES, QMS, and SCADA systems without standardized integration protocols. This fragmentation prevents real-time production monitoring, forcing manual data consolidation that delays decision-making and obscures critical operational patterns.
Unplanned equipment downtime from reactive maintenance approaches
Traditional time-based maintenance schedules fail to account for actual equipment condition, leading to unexpected failures that halt production lines. Without sensor data analysis and failure pattern recognition, manufacturers lack the predictive insights needed to prevent costly unplanned downtime.
Quality defects detected post-production instead of in-process
Manual inspection methods and statistical sampling miss defects until final quality checks or customer complaints. This delayed detection results in high scrap rates, rework costs, and compromised customer relationships that could be prevented with automated vision inspection and real-time process monitoring.
Legacy equipment data trapped in proprietary systems
Decades-old PLCs, CNC machines, and process controllers use incompatible industrial protocols (Modbus, OPC-UA, Profinet) that resist modern data integration. This creates information silos where valuable production data remains inaccessible for analytics, requiring costly manual data extraction or complete equipment replacement.
Manual reporting consuming engineering resources
Production teams spend hours aggregating data from multiple sources into spreadsheets and static reports, diverting skilled personnel from value-added activities. This manual consolidation introduces data entry errors, delays insights, and prevents the real-time monitoring needed for agile manufacturing operations.
Supply chain disruptions from limited visibility
Lack of integration between inventory systems, supplier networks, and production schedules creates blind spots in material availability and delivery timing. Without predictive analytics on supplier performance and demand patterns, manufacturers face production delays and expedited shipping costs.
Inconsistent data quality undermining analytics initiatives
Sensor calibration drift, manual data entry variations, and inconsistent measurement standards across shifts produce unreliable datasets. This data quality problem prevents accurate machine learning model training and invalidates analytics insights needed for process optimization.
Inability to scale digital initiatives beyond pilot programs
Proof-of-concept AI and IoT projects demonstrate value in isolated use cases but fail to expand enterprise-wide due to technical debt, integration complexity, and lack of standardized data architecture. This limits ROI and prevents manufacturers from achieving comprehensive digital transformation.
Build custom manufacturing platforms that solve production-critical challenges
What we engineer for manufacturing use cases
Unified data orchestration platforms with multi-protocol integration
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.
Predictive maintenance systems with machine learning anomaly detection
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.
Computer vision quality inspection platforms with real-time defect classification
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.
Industrial IoT gateways with legacy protocol translation
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.
Automated reporting engines with real-time KPI calculation
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.
Supply chain visibility platforms with predictive analytics
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.
Data quality frameworks with automated validation and monitoring
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.
Scalable cloud-native architectures with modular microservices design
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.
How to start
Transform your enterprise with AI and data engineering—faster efficiency gains and cost savings in just weeks
Challenge briefing
Tech assessment
Discovery phase
Proof of concept
MVP in production
Why Xenoss is trusted to build production-grade manufacturing platforms
Deep expertise in connecting legacy manufacturing systems
We engineer data pipelines that extract production data from PLCs, SCADA systems, and CNC controllers using Modbus RTU, OPC-UA, Profinet, and proprietary industrial protocols. Our integration expertise spans decades-old equipment to modern cloud-native platforms, enabling manufacturers to leverage existing machinery investments while accessing real-time analytics without requiring costly equipment replacement.
Built predictive maintenance systems reducing unplanned downtime by 30%
Developed anomaly detection systems for discrete and process manufacturers that analyze vibration, temperature, pressure, and performance data to predict equipment degradation 2-4 weeks in advance. Our predictive models achieve 85% accuracy in failure forecasting, enabling scheduled maintenance during planned downtime rather than emergency production stoppages.
Automated defect classification systems integrated into production workflows
Engineered real-time quality inspection platforms using industrial cameras and custom-trained deep learning models that detect surface defects, dimensional variations, and assembly errors at millisecond speeds. Our vision systems integrate directly into production lines, providing automated part rejection and root cause analytics that identify process issues upstream.
Implemented unified data platforms eliminating 76% system fragmentation
Created centralized data orchestration layers for manufacturers operating SAP Business One, Epicor, NetSuite, Plex MES, and legacy databases. Our integration platforms implement event-driven architectures using Apache Kafka and standardized APIs, consolidating previously siloed production metrics, quality data, and supply chain information into real-time operational dashboards.
Engineered production analytics systems eliminating manual reporting overhead
Built continuous data processing frameworks that extract production metrics from multiple sources, calculate OEE, cycle times, yield rates, and downtime categories according to manufacturing standards, and load results into interactive dashboards. Our systems eliminate 80% of manual spreadsheet consolidation, freeing engineering resources for value-added activities.
Delivered scalable cloud-native architectures supporting enterprise expansion
Architected manufacturing data systems using containerized microservices (Docker, Kubernetes) with API-first designs that support modular expansion. Our platforms enable manufacturers to start with focused use cases, such as predictive maintenance for critical equipment, and then scale to enterprise-wide deployment across multiple facilities without rebuilding integration logic.
ROI-driven implementations with measurable operational and financial impact
Deployed custom AI and data platforms for manufacturers that reduced maintenance costs by 20-40%, improved quality detection by 25-32%, and increased equipment effectiveness by 58%. Our solutions deliver 18-month average ROI through reduced downtime, lower scrap rates, and improved production throughput.
Specialized in manufacturing constraints and production realities
Engineered solutions accounting for limited IT resources, budget constraints requiring phased implementation, production schedules preventing extended downtime, and the need to integrate with existing systems rather than wholesale replacement. Our deployment methodology includes comprehensive training, change management support, and production-safe implementation strategies.
We build unified data platforms that connect legacy equipment with modern analytics
We engineer AI platforms that prevent failures and optimize production processes
We develop specialized solutions for industry-specific operational challenges
Featured projects
Schedule a technical consultation to discuss your specific challenges: unplanned equipment downtime, quality defect rates, manual data consolidation, legacy system integration, or supply chain visibility gaps. Our engineers will analyze your production environment, technology stack, and operational requirements to design custom data and AI platforms that address your highest-priority pain points with measurable performance improvements.
Xenoss team helped us build a well-balanced tech organization and deliver the MVP within a very short timeline. I particularly appreciate their ability to hire extreme fast and to generate great product ideas and improvements.
Oli Marlow Thomas,
CEO and founder, AdLib
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