From Fragmented Evidence to Agentic Industrial Intelligence
Industrial operators have long struggled to turn sprawling data—P&IDs, PDFs, sensor logs, invoices, and schematics—into reliable AI applications. Information is scattered across systems and formats, so engineering and operations teams often spend weeks manually extracting, cleaning, and reconciling data before they can even prototype a model. By the time something is ready, the underlying data has shifted and pipelines start to break. This is the gap that agentic AI platforms are now closing. Instead of treating industrial data as a pre-processing burden, agentic systems handle multimodal inputs as they are, dynamically composing context and structure on demand. That shift turns AI agents in industrial settings from brittle, one-off experiments into continuous, production-grade intelligence that evolves with the plant. The result: industrial AI workflows that can be measured in days, not months, from idea to operational impact.
Corvic AI’s Agentic Data Engine and the Rise of Enterprise AI Composition
Corvic AI tackles the “fractured evidence” problem with an agentic data engine designed to sit as a logic layer between messy enterprise data and production AI. Its Intelligence Composition Platform ingests multimodal operations data—images, PDFs, logs, and tables—and converts it into structured outputs ready for any workflow or AI agent, without forcing teams into rigid schemas or fragile ETL pipelines. With the launch of Corvic V3 and general availability on major cloud marketplaces, Corvic’s agentic data engine is now accessible to a broader range of industrial, manufacturing, field services, and life sciences organizations. This democratizes enterprise AI composition: engineers can build queryable asset knowledge graphs from P&IDs, compliance teams can generate structured regulatory submissions in days, and field services teams can unify manuals, sensor logs, and inspection images for rapid root-cause analysis. In practice, AI agents industrial users design on Corvic can now operate reliably across constantly changing data landscapes.
Cognite Flows: Connecting Real-Time Plant Data to AI Agents on the Front Line
Cognite Flows brings AI agents industrial workers can actually use to the front line by unifying real-time plant data, AI-driven recommendations, and applications in a single interface. Built on Cognite’s Industrial Knowledge Graph, Flows keeps AI insights tied to live operating context, so recommendations always reflect the current state of equipment, processes, and production. Developers benefit from agentic AI coding tools and an AI-native architecture that dramatically reduces app build times—prototypes and workflows that once took teams of specialists several months can now be delivered in a matter of days. More than 30% of Cognite’s customers and partners are already using Flows, including those capturing expert plant knowledge as digital assets and improving asset health visibility. By embedding real-time plant data AI directly into daily workflows, Cognite Flows helps operators act on insights instantly instead of toggling between disconnected systems and dashboards.
Bridging Legacy Systems With Modern Industrial AI Workflows
A major barrier to industrial AI adoption is the tangle of legacy systems—decades-old historians, document repositories, and maintenance tools that cannot simply be ripped and replaced. Both Corvic AI and Cognite approach this challenge by treating existing infrastructure as a given and layering intelligence on top. Corvic composes intelligence directly across whatever data sources exist, avoiding brittle re-platforming projects. Cognite Flows uses its knowledge graph to contextualize operational data from disparate systems, then exposes it through AI agents and applications tailored to operators and engineers. Together, these approaches redefine industrial AI workflows: instead of lengthy integration programs, teams focus on rapidly composing, testing, and refining AI use cases. Enterprise AI composition shifts from a centralized, specialist-only effort to something domain experts across operations, compliance, and maintenance can participate in—shrinking development cycles while extending the life and value of legacy investments.