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The Mirror. Digital Twin Opportunity Lab
Could a digital twin improve your operations?
Answer a few questions about your site. The lab checks how ready it is, suggests the first twin worth building, and says plainly when monitoring and analytics are the better start.
Your digital twin readout
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Use case guides
- Build a smart factoryCombine a physically accurate digital twin of the plant, vision AI on the line and simulation-trained robots to plan layouts, catch defects and test changes before they reach the real factory floor.
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Use case guides
- Warehouse digital twinA simulation of a warehouse's layout, robots, people and flows used to test slotting, fleet sizes and safety rules, and to train and check the AI that runs on site, before anything changes in the real building.
- Logistics optimizationPlan delivery routes, fleet assignments and schedules with mathematical optimization and rerun plans quickly when orders, vehicles or road conditions change, using GPU-accelerated solvers alongside existing planning systems.
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Use case guides
- Traffic managementUse intersection cameras, vision AI and corridor simulation to count traffic, detect incidents and test signal or layout changes, under strict privacy, transparency and public accountability rules.
- Video analyticsTurn camera streams into events, alerts, searchable footage and summaries using real-time detection and tracking plus vision language models, with privacy rules decided before the first camera is connected.
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Use case guides
- Simulate an AI data centerModel an AI data center's network, power, cooling and layout in software before hardware arrives, to find design conflicts early, test automation and plan how many GPUs fit within a fixed power budget.
- Build an AI factoryPlan, build and run a dedicated GPU cluster for training, fine-tuning and inference, treating compute, network, storage, power, cooling, scheduling and day-two operations as one system rather than separate purchases.
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Use case guides
- Build autonomous robotsDevelop robots that perceive, plan and act in changing surroundings by training and testing their behavior in simulation, then running perception and control on an onboard edge computer.
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