Automated and Optimized
P&ID Ingestion
Revolutionizing piping and instrumentation diagrams: harnessing the dimensional power of P&ID in industry and manufacturing through AI
Automated diagram interpretation
Machine Learning and Vision AI algorithms analyze, recognize, and interpret drawing symbols, text, and process patterns in P&IDs to identify machines, sensors, piping, and other components. These components and their interconnections are digitally modeled into inputs and data sources as an integral pipeline within Data Orchestration.
Bring unprecedented efficiency to traditional ingestion processes of engineering and design data. Use automation, asset libraries, OCR, and AI technologies to parse and extract diagram components and their connections, along with asset specifications and metadata associated with each component.
Intelligent P&IDs are easily managed, updated, reviewed, and monitored, as changes are instantly reflected to all of the linked and shared data sources affected by the changes within the organization, accelerating the goal of achieving and maintaining optimal system performance.
Automated P&ID interpretation and ingestion creates an end-to-end solution for translating engineering data & drawings from conventional systems, to parsing component symbols, labels, and specifications into meaningful and valuable structured data.
Extract valuable data trapped in P&IDs
Turn flat P&ID documents into live, interactive sources of information. Identify assets and contextualize information from other sources. Build an asset hierarchy using the relationships defined in the process
Use Vision AI, a combination of optical character recognition (OCR) and machine learning (ML), to identify assets, instruments, and relationships across various symbols, fonts, texts, and layouts.
Train continuous learning pipelines to identify site or company-specific information, improving the accuracy and efficiency required to ingest thousands of P&IDs at scale.
Intelligent P&ID beneficial use cases
Identifies potential machinery issues across an entire enterprise before they lead to failures, leveraging predictive analytics to enhance operational reliability and minimize maintenance costs.
Analyzes and simulates process flow efficiency within diagrams to identify and address potential bottlenecks and to suggest improvements for enhancing productivity, optimizing operational flows, and reducing energy and waste.
Provides tools for scenario planning and deep insights into operational efficiency to identify areas for improvement and innovation, helping managers make informed decisions about future operations and strategic directions.
Enables quick adaptation to market changes through agile reconfiguration of process operations, maintaining competitive advantage while further supporting strategic investment decisions with data-driven insights as justification for resource allocations.
