Domain-specific AI engineering tools — each running entirely in the browser. No backend, no signup. Just open and explore.
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AI+Human driven iterative design optimisation for semiconductor wafer chuck geometry. Adjust wall thickness, cooling channels, physics and materials — the copilot takes inputs, generates geometry, runs FEA simulation, AI reads results and proposes parameter improvements to meet stress, bow and thermal targets.
10-layer working Digital Engineering Data Pipline pilot connecting CONOPS, MBSE, requirements, FMEA, CAD ingestion, geometry embeddings, digital twin telemetry, supplier risk and GraphRAG reasoning — all running in the browser with AI answers (pre-builts or API driven) across all layers.
Related reading
Simulation @ Cloud with AI — the ideas behind this demo
Possibilities
Closed-loop AI optimisation of semiconductor fab process parameters to maximise yield across wafer lots in real time.
Guided AI material selection across mechanical, thermal and cost trade-offs for precision engineering components. Demonstrated in Design Co-Pilot Demo
AI-assisted heat dissipation design for power electronics — fin geometry, TIM selection and cold-plate routing.Demonstrated in Design Co-Pilot Demo
Upload a CAD drawing and receive instant Design-for-Manufacturability feedback on wall thickness, draft angles and tolerancing. Demonstrated as CAD Ingestion + Query across Geomtery, Metadata and Feature layers (5, 6 & 7) in AI Native Digital Engineering Data Pipeline Demo
Assess single-source risk across your BOM, simulate lead-time disruptions and get recommended alternate suppliers.Demonstrated as Supplier and BOM Risk layers (9 & 10) in AI Native Digital Engineering Data Pipeline Demo
AI-guided topology optimisation for lightweight structural components — minimise mass while meeting stiffness targets.Demonstrated in Design Co-Pilot Demo