Industrial Data Foundation
Cybus Connectware turns fragmented machine data into a unified foundation – so you can deploy use cases faster, run them reliably across sites and prove measurable impact.
9%
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13%
17%
Run multiple plants with machines from different vendorsNeed governed, reliable real-time data for MES, AI, analytics or energy toolsRequire strong security, segmentation and auditabilityReady to move from pilot projects to enterprise-wide programs
IT takes control, operations accelerates onboarding, business validates outcomes faster and leadership scales with confidence.
Centralized governance, fewer one-offs, secure multi-site operations.
Onboard machines quicker, monitor in real time, reduce vendor dependence.
Quicker results in quality, energy and OEE – with data you can trust.
Clear path from pilots to enterprise programs with visible ROI.
Legacy setups trap use cases at one site. Without a Unified Namespace (UNS) to standardize and govern your data, every rollout becomes a new project.
The longer you wait for a unified layer, the more every use case costs to scale.
Rigid stacks. Slow to scale.
Legacy automation and pyramid setups lock you into static structures and old protocols. Each change demands custom work and testing – slowing every rollout.
No shared model. No speed.
Without a shared data model and aligned teams, deploying use cases becomes difficult. Duplicate logic and siloed setups make every rollout slow and error-prone.
Same use case. Different data.
Missing context and vendor-specific schemas make data hard to trust and reuse. Inconsistent naming blocks cross-site scale – at every site.
No UNS, no reuse.
Without a UNS aligned to ISA-95, structures stay local and inconsistent. Mapping and retesting repeat at every site – weeks lost before value is realized.
No reuse, no scale.
Without reusable patterns, one-off integrations stall at the first site. Local logic and hardcoded paths trap pilots – stopping rollouts beyond the first site.
No governance, more risk.
No consistent access control, encryption or versioning mean slow audits and weak accountability. Vulnerabilities multiply across sites.
With semantics, templates and governance by design, Cybus Connectware delivers scalable rollouts, flexible deployments and secure ops across every site.
Kubernetes‑native clustering plus MQTT buffering, retry and agent‑based prioritization keeps global operations resilient at scale.
Deploy on‑prem, in the cloud or hybrid – containerized and vendor‑agnostic to fit any IT strategy and existing security policies.
Git‑controlled YAML templates and central policies standardize rollouts – plants keep site‑level autonomy for fast local changes.
Reusable templates, automation & CI/CD let small teams roll out new factories in weeks – reducing setup effort and project risk.
Connectware filters, normalizes and enriches data in real time – across OPC UA, S7, MQTT and REST – for MES, dashboards and AI.
Encrypted pipelines, MFA, LDAP integration and network segmentation – with role‑based access to secure global rollouts by default.
This architecture shows how Cybus Connectware operates as the Industrial Data Management between OT and IT – and how it fits into your existing factory set-up.
It standardizes data from PLCs and machines to MES, ERP, data lakes and AI, with configuration managed centrally via Git and rolled out on Kubernetes.
Start small, scale fast. From one machine to multiple sites, Cybus Connectware keeps the structure, logic and governance consistent at every step.
YAML-based Service Commissioning Files create clean machine data and Unified Namespaces (UNS) – a structure like no other.
Reusable templates eliminate one-off setups and manual config for every new asset – without having to start over from scratch.
Expose governed, trusted data to MES, analytics and energy platforms – and use them throughout your deployments.
Deploy securely and repeatably with GitOps-style CI/CD and Kubernetes – while minimizing any risk.
From YAML to real-time – 30-second demo of how UNS (ISA-95) works inside Cybus Connectware.
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