IEC 62443 OT Cybersecurity
Industrial cybersecurity standard applied to AI integrations with OT networks. We embed segmentation, monitoring, and incident response from day one.
Industry
Industrial AI for Discrete and Process Manufacturing
Predictive maintenance, quality, supply chain, and energy optimisation built for plant-floor reality.
Overview
Manufacturing, the practice in summary
Manufacturing AI succeeds when it respects the physics of the plant floor. Magnis AI delivers industrial AI across discrete and process manufacturing with predictive maintenance, computer vision quality control, supply chain optimisation, production planning, and energy efficiency. Our work pairs OT and IT teams, integrates with historians, MES, ERP, and SCADA, and is designed to operate reliably in the latency, connectivity, and cybersecurity constraints of real manufacturing environments.
Magnis App for Manufacturing
Magnis ERP: GST-compliant ops for Indian manufacturers.
Quote to SO to invoice in minutes. Real-time stock with batch tracking, ClearTax e-invoice, Delhivery dispatch. Magnis ERP is purpose-built for Indian SMB manufacturers.
- Quote to SO to Invoice. Full order-to-cash flow with GST line items, discount tiers, and e-invoice-ready PDF generation. One source of truth, no spreadsheet glue.
- Real-time stock and batch tracking. Inventory moves with every delivery note. Batch numbers, FIFO costing, expiry tracking, low-stock alerts. Real-time, not end-of-day.
- Delivery notes and logistics. Dispatch slips, Delhivery and Bluedart booking, WhatsApp updates to customers with AWB and tracking link. No tab switching.
- ClearTax e-invoice. Push invoices directly to ClearTax IRP. No separate portal, no IRN copy-paste, no reconciliation. IRN and QR code land back on the invoice.
- Partial invoicing and PO tracking. Multi-milestone invoices on one SO. Purchase orders with receipt-versus-PO matching. Three-way match for procurement teams.
Regulatory Context
The frameworks shaping AI in manufacturing
We translate each into engineering controls and audit evidence. AI in manufacturing ships faster when the regulator is treated as a stakeholder from day one.
ISO 9001 and Quality Systems
AI in quality control integrated with your existing QMS. Audit trails, validation, and change control aligned to ISO 9001 expectations.
ISO 27001 and Data Protection
Information security controls for industrial data flows, particularly when AI involves cloud processing or vendor data sharing.
EU AI Act for Manufacturing
AI used in safety components of machinery falls under EU AI Act high-risk classification. We handle conformity assessment where relevant.
Use Cases
High-value AI use cases for manufacturing
Each use case is shipped with the governance, validation, and monitoring needed to clear internal risk review and regulator inspection.
Predictive Maintenance
Failure prediction on motors, pumps, compressors, and CNC machines using vibration, current, temperature, and process data. Designed for retrofit and greenfield environments.
Quality Control and Defect Detection
Computer vision and multi-modal defect detection integrated with PLCs and MES. Edge deployment with central retraining and governance.
Supply Chain Optimisation
Demand forecasting, inventory optimisation, supplier risk, and logistics planning with explainable models and scenario planning interfaces.
Production Planning and Scheduling
AI-augmented planning and scheduling with constraint solvers and reinforcement learning for complex multi-product environments.
Energy and Sustainability
Energy efficiency optimisation, carbon accounting automation, and Scope 1, 2, and 3 reporting AI with traceable methodology.
Worker Safety and Operations
Computer vision for PPE compliance, intrusion in restricted zones, and ergonomic risk identification with privacy-preserving design.
Generative AI for Knowledge and SOPs
Conversational assistants over maintenance manuals, standard operating procedures, and equipment documentation. Operators ask in natural language and get the right SOP step with images and citations, reducing time-to-resolution on the line.
Services we deliver in Manufacturing
The Magnis AI services that fit this sector
Enterprise AI Consulting
Three integrated pillars under one practice: AI Governance and Compliance, Generative AI and LLM Consulting, and Intelligent Process Automation. Designed for banks, insurers, healthcare, government, and large operators that need AI to stand up to regulators and to the board.
ExploreAI Automation
Most SMBs lose 15 to 30 hours per week per role to repetitive work. Magnis AI builds automation that removes the work, not the team, using LLM agents, n8n, Zapier, OpenAI, and bespoke integrations with your stack.
ExploreWeb Development
Modern web development on Next.js, React, and headless CMS. Marketing sites built for SEO and speed, SaaS dashboards built for product velocity, and e-commerce stores built for revenue.
ExploreMethodology
How an industry engagement runs
Four phases, each calibrated to the sector. Regulatory diagnostic comes first; deployment follows once the controls envelope is clear.
- 01
Plant and Use Case Diagnostic
Walk the plant, map data sources, and rank candidate use cases by value and feasibility. Output: 24-month industrial AI roadmap.
- 02
Data and Connectivity Foundation
Historian integration, OT-IT bridges, data quality remediation, and edge compute design where required.
- 03
Pilot to Plant
Deploy and validate on one line or one asset class, then scale via reusable patterns. Operator acceptance designed in.
- 04
Scale and Sustain
Roll out across sites with central platform, governance, and CoE. Handover or managed-service operation.
Outcomes
Measurable results
Specific, defensible outcomes from typical engagements.
- Reduction in unplanned downtime via predictive maintenance
- Defect detection rates above human inspection with full audit trails
- Inventory reduction without service-level loss
- Measurable energy and carbon intensity reduction
- OT-safe AI architecture that passes plant cybersecurity review
Common questions about AI in manufacturing
Do we need to move OT data to the cloud for AI?
Not necessarily. Many manufacturing AI workloads run on the edge or in a regional data centre with only aggregated telemetry sent to cloud. We design the data architecture to your latency, cybersecurity, and regulatory constraints. Cloud is a tool, not a default.
How do you integrate AI with our MES, SCADA, and historian?
Through standard interfaces (OPC UA, MQTT) and tested patterns against the major historians (PI System, Aveva, Ignition) and MES platforms. We do not require rip-and-replace. AI sits alongside the existing stack and writes back where appropriate.
What about generative AI on the plant floor?
Promising areas include maintenance manuals as conversational assistants, standard operating procedure generation, root cause analysis support, and training material creation. We deliver these with human-in-the-loop and evaluation against domain experts.
How do you handle the cybersecurity risk of AI in OT environments?
Through IEC 62443 aligned segmentation, one-way data diodes where appropriate, hardened edge compute, and integration with your SOC. AI never has direct write access to safety-critical systems without engineered checks.
What is a realistic ROI for industrial AI?
Predictive maintenance typically returns three to ten times in the first year on covered assets. Quality AI returns through reduced scrap and rework, often within six months. Supply chain optimisation produces working capital release that funds the next phase.
Where do most manufacturing AI pilots fail and how do you avoid that?
Three failure modes recur: insufficient OT data quality (sensors that drift, sparse coverage, no labels), trying to scale before proving the pattern works on one line, and OT-IT teams that do not align on data ownership and uptime requirements. Magnis AI front-loads a 4-week data diagnostic before any model build so we know what is solvable and what needs sensor or process change first. We also pilot on one line and prove the operating model before scaling.
Can AI work in factories with patchy connectivity or legacy systems?
Yes. Edge inference (running models on small industrial PCs near the line) handles latency-sensitive use cases without cloud dependence. Legacy MES, SCADA, and historian integration is a solved problem with standard interfaces (OPC UA, MQTT) and tested patterns. The harder issue is usually internal IT change-control, which we manage by aligning with your existing OT cybersecurity and change-management processes from day one.
Scope an Industrial AI Initiative
Book a 45-minute consultation. We will walk through your highest-cost downtime, quality, and inventory problems and shape the path to AI-driven improvement.