Your Next Operations Engineer
Isn't Human

Traditional RPA in manufacturing automates fixed, repetitive screen tasks. AI Agents, by contrast, understand context from machine data, logs and documents, reason over it, and take actions across systems. They can triage downtime events, suggest root causes, trigger work orders and answer operator questions instead of just clicking through a single UI.

n8n acts as the workflow backbone for your plant. It connects ERP, MES, SCADA, historians, CMMS, quality systems and cloud analytics into one flow. Events such as a machine alarm, stock level change or quality deviation can automatically trigger downstream actions—notifications, work orders, approvals, reports and updates back into your systems of record.

We integrate with major ERPs (SAP, Microsoft Dynamics, Oracle, Infor and more), MES platforms, SCADA systems, OPC/OPC UA gateways, historians, CMMS tools and homegrown applications. Where APIs are not available, we can use connectors, databases, message buses or RPA bridges to exchange data safely and reliably.

Yes—when designed correctly. We focus on decision support and workflow automation, not bypassing safety systems. AI Agents provide recommendations, trigger workflows and update systems; control decisions that affect machine safety remain governed by PLC logic, safety circuits and plant standards. Human approvals can be configured for high-impact actions.

We can deploy components on-premise, in your private cloud, or in a hybrid model. Edge connectors and gateways can synchronize data securely to the cloud when available, while critical workflows can continue to run locally. Architecture is tailored to your IT/OT and regulatory requirements.

Yes. Our frameworks support discrete manufacturing (automotive, machinery, electronics), process industries (food & beverage, chemicals, pharma) and hybrid operations. We adapt workflows to your regulatory and quality environment, whether that's GMP, HACCP, ISO or industry-specific standards.

Plants typically see reduced unplanned downtime, higher OEE, lower scrap and rework, faster response to issues and less manual admin work for engineers and supervisors. The result is higher throughput using the same assets and people, with payback often measured in months, not years, for focused use cases.

We start with non-disruptive, read-only integrations and shadow dashboards. Once data flows are validated, we layer on targeted automations around existing processes rather than forcing a full replatform. Changes are introduced in phases, with operator and engineer feedback, to minimize disruption to daily operations.

A focused use case—such as automated downtime tracking, digital checklists or CMMS integration—can typically go live in 3–6 weeks. Broader transformations across multiple lines or plants are phased, so you see value quickly while we build a scalable, reusable automation foundation.

We begin with a short discovery workshop to map your production, maintenance and quality workflows, identify high-impact pain points and review your systems landscape. Then we propose a practical automation roadmap and build a proof-of-concept using AI Agents + n8n on one priority line or process to demonstrate value quickly.