Manufacturing AI Automation Services | Salyant

Salyant deploys four high-impact AI patterns proven in UK discrete and process manufacturing environments. Each capability targets a specific P&L line item and integrates via standard industrial protocols (OPC UA, MQTT, Modbus) without rip-and-replace.

Our delivery model follows a 90-day sprint cadence: discovery and data readiness (weeks 1-3), model development and shadow-mode validation (weeks 4-8), production deployment with operator training (weeks 9-12), and continuous retraining pipelines thereafter.

• Predictive Maintenance: Vibration, thermal, and current signature analysis reducing unplanned downtime 30-50% (Make UK benchmark)
• Computer Vision Quality Inspection: Real-time defect detection at line speed cutting escape rates 60-80% and scrap costs 15-25%
• Demand Forecasting & Inventory Optimisation: Probabilistic forecasting reducing finished goods inventory 20-35% while maintaining 98%+ service levels
• Digital Twin Process Optimisation: Physics-informed ML models enabling virtual commissioning and energy optimisation yielding 5-15% utility savings

Successful manufacturing AI depends on data architecture, not model complexity. Salyant assesses your historian (OSIsoft PI, Wonderware, Ignition), MES, and ERP landscape to design a unified namespace (UNS) using MQTT Sparkplug B or OPC UA PubSub. This creates a single source of truth for both OT and IT consumers.

We implement the ISA-95 Level 3-4 integration pattern: edge gateways for protocol translation and preprocessing, Kubernetes-based MLOps platform (on-prem or Azure/AWS/GCP) for training and serving, and CI/CD pipelines for model versioning, drift detection, and automated rollback. All deployments meet IEC 62443 cybersecurity standards.

• Unified Namespace (UNS) implementation per ISA-95/ISA-88 standards
• Edge computing deployment for sub-second inference at the line
• MLOps pipeline with automated retraining, drift monitoring, and A/B testing
• IEC 62443 SL1-SL2 compliance for OT network segmentation
• Data quality scoring and lineage tracking for auditability

Salyant operates on an outcome-aligned fee structure: a fixed monthly retainer covering platform, engineering, and MLOps, plus a variable component tied to verified KPI improvements (OEE, scrap rate, inventory turns, energy per unit). This transfers implementation risk to us and ensures executive sponsorship remains engaged.

Our ROI calculator—calibrated against 50+ UK manufacturing deployments and Make UK productivity benchmarks—models payback period, NPV at your WACC, and EBITDA impact per site. Typical payback: 6-14 months for predictive maintenance; 4-8 months for vision inspection; 3-6 months for inventory optimisation.

• Outcome-based pricing: retainer + verified KPI uplift share
• ROI calculator with site-level P&L modelling and WACC-adjusted NPV
• 90-day sprint milestones with go/no-go gates
• Multi-site rollout framework with standardised playbooks
• Knowledge transfer and internal team upskilling included

UK manufacturing AI adoption stalls at the people layer, not the technology layer. Salyant includes a structured change management workstream: operator-in-the-loop design sessions, shift-pattern-aligned training, and works council engagement where applicable. We document model cards and decision logs for UK GDPR Article 22 compliance and upcoming AI Act conformity assessments.

For PE-backed platforms, we deliver a portable AI asset register—models, data pipelines, and IP—ensuring portfolio companies retain full ownership and can replicate across bolt-on acquisitions without vendor lock-in.

• Operator-in-the-loop UI design for trust and adoption
• Shift-aligned training programmes with competency sign-off
• Model cards and audit trails for UK GDPR / EU AI Act readiness
• Portable AI asset register for PE portfolio replication
• Works council and union engagement frameworks