Technology That Powers the Factory of the Future

We follow a strict non-disruptive deployment methodology. All AI model development and testing occurs in staging environments that mirror your production setup using historical data. IoT sensor connections are made via read-only interfaces that have zero impact on OT systems. Go-live activities are scheduled during planned maintenance windows, and we maintain comprehensive rollback procedures at every stage. Our OT-experienced engineers understand the zero-tolerance for production disruption in manufacturing environments.

We design our engagements to deliver measurable value within 6-8 weeks — even for complex, multi-facility deployments. A typical engagement follows a phased approach: Discovery & Assessment (2 weeks), Architecture & Roadmap (2 weeks), Pilot Deployment delivering first value (6 weeks), Full Rollout (10-16 weeks). Exact timelines depend on scope, but we always prioritize early wins that validate the business case before scaling.

Yes — and this is one of our core strengths. Our data integration team has deep experience with SAP S/4HANA, SAP ECC, Oracle E-Business Suite, Oracle Cloud ERP, and a wide range of MES platforms including Siemens Opcenter, Rockwell FactoryTalk, and GE Proficy. We use MuleSoft, Azure Data Factory, and custom REST/SOAP API connectors to build robust integrations that maintain data integrity and performance at enterprise scale.

We take a non-disruptive, defence-in-depth approach to OT cybersecurity. Rather than requiring legacy system replacement (which is often impossible in operational environments), we deploy passive monitoring solutions that read OT traffic without injection — tools like Claroty, Dragos, and Azure Defender for IoT. We implement network segmentation using industrial-grade firewalls to create DMZs between OT and IT zones, and extend Azure Sentinel SIEM visibility to cover OT protocols. The result is full visibility and threat detection without touching existing OT systems.

You don't need perfect data to start. We begin with a data readiness assessment to understand what sensor data, historian data, and maintenance records you already have. For predictive maintenance models, we typically need 12-18 months of historical sensor data per asset class to train initial models. If you have existing OPC-UA or MQTT-enabled sensors, we can connect them directly. For assets without sensors, we work with you to determine cost-effective sensor retrofitting options. We've successfully deployed models with imperfect, real-world manufacturing datasets — that's normal in this industry.

Yes — and for manufacturing environments, we strongly recommend it. Our Manufacturing Managed Services practice provides 24/7 NOC/SOC monitoring, monthly ML model retraining and performance reviews, SLA-backed incident response for critical production systems, and quarterly business reviews aligned to your KPIs. Most of our manufacturing clients retain us on multi-year managed service agreements because the continuous optimization compounds their initial ROI significantly. We offer flexible tiers from basic monitoring through to full strategic advisory retainers.

Our manufacturing practice covers a comprehensive range of compliance frameworks: IEC 62443 for industrial cybersecurity, NIST CSF for overall cybersecurity posture, ISO 27001 for information security management, SOC 2 Type II for SaaS and cloud services, GDPR for data protection across European operations, and industry-specific standards including IATF 16949 for automotive, FDA 21 CFR Part 11 for pharmaceutical manufacturing, and FSMA for food & beverage. We provide compliance advisory as part of every engagement and can deliver formal compliance documentation as a deliverable.