New AI start-up targets connected industrial operations
Published on 21-01-2026 at 17:19
Dr. Rafiq Swash has returned to the industry as Founder and CEO of INK AI, a company set up to "lead the transformation of connected industrial operations with AI infrastructure." For operations leaders, that statement isn't fluff-it points to a platform approach that ties data, decisions, and devices across plants, ports, and logistics networks.
The headline here: INK AI is positioning itself as the glue between OT and IT. If they get it right, expect faster planning and scheduling, tighter yard and fleet control, and fewer process bottlenecks across high-throughput environments.
What "AI infrastructure" means for operations
- Unified data layer: Connects SCADA/DCS, MES, WMS, TOS, ERP, and sensor data into a single model for analysis and action.
- Edge inference: Models run near equipment to reduce latency for tasks like crane dispatch, AGV routing, and anomaly detection.
- Closed-loop optimization: Plans are generated, executed, and re-optimized based on live constraints and events.
- Interoperability: Open standards (e.g., OPC UA) and APIs to reduce custom integration and downtime.
- Governance and safety: Access controls, audit trails, and fallback modes for regulated or mission-critical operations.
High-value use cases to watch
- Container terminal operations: Yard planning, crane scheduling, vessel load/discharge optimization, and gate/traffic flow.
- Intralogistics: Autonomous/assisted fleet orchestration, slotting, and pick-path optimization.
- Maintenance: Condition-based maintenance and spares forecasting to cut unplanned downtime.
- Energy and throughput: Real-time setpoint tuning to balance throughput, energy, and emissions targets.
Why this matters now
Operations teams are under pressure to increase throughput without new capex and to prove ROI on digital projects. A platform built for connected operations can shorten integration timelines and make pilots stick because the data, models, and operators live in one loop.
The original announcement highlights focus areas like container terminal operations and planning/scheduling software-clear signals that the first wins will come from time-sensitive, high-variance processes.
How to evaluate INK AI (or any vendor in this space)
- Systems coverage: Native connectors for PLCs/SCADA, MES, WMS/TOS, and ERP. How much custom work is needed?
- Latency and resilience: Can edge nodes run safely if the network link drops? What's the failover plan?
- Security posture: Role-based access, network segmentation, and alignment to frameworks like NIST SP 800-82 (ICS).
- Interoperability: Support for open standards such as OPC UA, plus well-documented APIs and events.
- Time-to-value: Can you deploy a focused use case in 8-12 weeks with measurable KPIs?
- Human-in-the-loop: Clear handoff between automation and operators. Explainable recommendations and easy override.
- KPIs that matter: Throughput per hour, crane/vehicle utilization, yard dwell time, OEE, energy per move.
Practical next steps for your team
- Map data sources and constraints: PLCs, historians, TOS/WMS, ERP, maintenance logs, traffic sensors.
- Pick one high-impact process: Yard plan adherence, crane dispatch, or AGV routing with visible delays or rework.
- Baseline first: Establish current cycle times, dwell, and energy consumption before any pilot.
- Pilot with clear guardrails: 8-12 week scope, defined success metrics, rollback plans, and operator training.
- Operationalize: SOP updates, escalation paths, and continuous monitoring for drift and safety.
Skills and training for operations teams
Your team will move faster if planners and supervisors understand AI-assisted scheduling, data quality basics, and change control. If you're building capability internally, these curated resources can help:
INK AI's entry signals growing demand for connected, measurable improvements on the ground. If you're in operations, the play is simple: pick a process, wire the data, test fast, and scale what works.
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