Field-proven Industrial AI

Engineering teams were spending weeks reviewing large proposal packages — drawings, specifications and commercial documents — under tight bid deadlines.

An AI-assisted proposal analysis workflow that reads the full package and extracts scope, resources, risks, deliverables, assumptions and exclusions — with engineers validating every output.

• Weeks of review compressed to days
• Clause-level traceability to source documents
• Excel-ready structured output
• Human-in-the-loop validation throughout

Decades of brownfield P&ID data sat locked in PDFs, CAD files and inconsistent engineering databases — invisible to modern digital tools.

Engineering data converted into structured nodes and relationships with DEXPI-ready output, loaded into a graph database that connects every tag, line and instrument.

• Faster digital twin onboarding
• End-to-end tag traceability
• Search across all engineering data
• Foundation for AI copilots

Engineers performed repetitive modeling, data extraction and report generation by hand inside Smart 3D and related engineering tools.

An agentic co-pilot architecture that operates Smart 3D and engineering databases on behalf of the engineer — automating modeling tasks, data retrieval and design checks.

• Repetitive modeling work automated
• Faster engineering data retrieval
• Automated design checks
• Higher engineering throughput

Operations teams lacked forward visibility into asset health, process behavior and future failure risk across critical equipment.

Time-series data, engineering metadata, AI models and physics-based logic fused into one live digital twin dashboard for asset performance.

• Live asset health monitoring
• Early anomaly detection
• Smarter maintenance planning
• Confident operational decisions