From Fragmented Systems to a Single Industrial AI Workspace
Industrial operations have long struggled with a patchwork of systems for analytics, control, and reporting, forcing operators to juggle multiple screens just to complete routine tasks. Cognite Flows tackles this challenge by providing a single-screen workspace where AI-driven recommendations, operational data, and industrial applications coexist. Built on Cognite’s Industrial Knowledge Graph, the platform keeps data tightly bound to live operating context, ensuring that recommendations reflect what is actually happening in the plant in real time. This design directly supports plant monitoring software needs, giving front-line teams a unified view instead of siloed dashboards. For industrial AI tools to deliver value on the shop floor, they must be embedded in day-to-day workflows. Cognite Flows aligns with this requirement by presenting contextualized data and actions in one environment, significantly reducing the time and cognitive effort required for operators to move from insight to intervention.
App Development Acceleration: From Months to Days
Historically, building custom industrial applications has been a slow, resource-intensive process, often stretching over several months. Cognite Flows is reshaping that timeline by leveraging agentic AI coding tools and an AI-native architecture designed specifically for industrial contexts. Developers can rapidly assemble and deploy tailored applications that sit directly on top of live operational data, with some implementations measured in days rather than months. This app development acceleration addresses a critical bottleneck for manufacturers and energy producers that need to respond quickly to changing operating conditions and regulatory demands. Instead of waiting for long IT cycles, teams can experiment, iterate, and refine applications in near real time. By tightly coupling development workflows with operational data integration, Cognite Flows makes it easier to transform ideas from engineers and operators into working tools that improve safety, reliability, and efficiency on the plant floor.
Unifying AI Recommendations with Live Plant Monitoring
The core innovation in Cognite Flows lies in how it merges AI-generated guidance with live plant monitoring data in one interface. Rather than treating analytics as a separate back-office function, the platform surfaces AI recommendations directly alongside real-time process values, equipment status, and workflow tools. This operational data integration helps operators assess suggestions in context: they can see, for example, how an AI-recommended adjustment relates to current temperatures, pressures, or asset health indicators. The result is faster decision-making cycles, because staff do not need to consult multiple applications or manually reconcile conflicting data sources. By grounding industrial AI tools in live operational reality, Cognite Flows supports more confident, auditable decisions. It also paves the way for more autonomous workflows, where AI agents can gradually take on tasks such as anomaly detection, optimization, and routine troubleshooting while keeping humans in the loop.
Front-Line Focus: Making AI Usable for Operators and Engineers
Cognite Flows is explicitly designed for front-line users—operators, maintenance technicians, and engineers—rather than only central data teams. Its unified workspace reduces the need to switch between disconnected plant monitoring software, maintenance systems, and reporting tools when handling urgent or routine tasks. Interfaces can be tailored to specific roles, so each worker sees the data, alerts, and workflows most relevant to them. This emphasis on usability is crucial for scaling industrial AI tools, which have often been confined to pilot projects because they were too complex or detached from real operations. By aligning AI capabilities with everyday tasks, Cognite Flows encourages adoption and continuous feedback from users in the field. This feedback loop then feeds into rapid UX and app refinement, helping organizations evolve their digital tools in step with operational realities rather than in isolated IT cycles.
Customer Examples: Capturing Expertise and Improving Asset Visibility
Early adopters of Cognite Flows highlight how the platform supports both knowledge capture and operational transparency. Idemitsu is using applications built on Flows to digitize specialized plant knowledge accumulated over many years, turning it into a reusable, AI-accessible asset. By leveraging real-time knowledge graphs, the company expects these tools to evolve into proactive AI agents capable of helping manage complex operations. B. Braun, another customer, reports that Cognite Flows enabled a unified, contextualized view of its asset data landscape. Within four weeks, the company refined how operational data is visualized and used, with near-instant updates driven by user feedback. Cognite also notes work with a global pharmaceutical company that significantly reduced the time required to deliver automated AI workflows. Together, these examples show how app development acceleration and operational data integration can translate into tangible improvements in asset health visibility, knowledge retention, and day-to-day plant performance.