Physical AI, built for the plant floor
Porcelio is an industrial physical-AI platform: GPU-accelerated vision, time-series models, digital twins, and bounded agents that reduce fired defects, tone and caliber drift, breakage, and kiln energy, where GPUs are required, not optional.
GPU-essential, end to end
Every layer of the loop is a GPU workload, perception at line speed, simulation of firing physics, model serving with cited reasoning, and large-scale optimization.
Perception
Jetson edge nodes run DeepStream and TensorRT for green and fired inspection at line speed.
Simulation
Omniverse simulates firing, shrinkage, glaze, and planarity before a batch is burned.
Serving
Triton, NIM, and NeMo serve plant models and cited process reasoning.
Optimization
cuOpt and RAPIDS optimize kiln energy, sorting, and schedules at scale.
Robotics
Isaac drives robot-cell inference for glazing, sorting, and packing.
Grounding
Bounded agents act within limits, citing telemetry and prior outcomes.
Line-speed vision at the edge
High-speed multi-camera and profile sensing runs on Jetson Orin with DeepStream and TensorRT, grading every green and fired piece at line speed. Inference happens on the plant floor, so grading keeps working independent of the network.
Firing physics, simulated before the burn
Omniverse-class simulation models the firing curve, shrinkage, vitrification, tone, and glaze behavior for a specific recipe on DGX/HGX plus RTX, so the plant can hit spec before committing a single piece to the kiln.
- Physics-informedModels shrinkage and vitrification, not just temperature.
- Recipe-specificCalibrated per body-and-glaze recipe from real outcomes.
- Closed-loopTrained on Vitreo labels; feeds Thermiq and Glazio.
Bounded agents with cited reasoning
Triton, NIM, and NeMo serve plant models and process reasoning that cites its sources. Agents are bounded, they act within limits your engineers define, and every decision is grounded in telemetry, simulation, and prior fired outcomes.
Aligned with Physical AI priorities
One data lake, one model router
The five products share one plant-edge runtime, one firing-and-quality data lake, and one model router, so data compounds instead of fragmenting across point tools.
Shared data lake
Firing and quality data from every line feeds one compounding memory.
One model router
Models are served and routed centrally across products.
Deep integration
Reads telemetry from presses, dryers, glaze lines, and kilns.
Answers for plant leaders
Why are GPUs essential, not optional?
How is process reasoning kept trustworthy?
Does data stay on-prem?
Instruments the line you already run
Porcelio ingests telemetry from presses, dryers, glaze lines, and kilns, and runs alongside your existing controls.
Kiln & press OEMs
Glaze & decoration
Inspection & data
Ceramics groups
by design, not by choice
Line-speed vision, firing-physics simulation, and plant-scale optimization are all GPU workloads. This is physical AI, where GPUs run the loop.
Go deeper on the architecture
We'll walk your technical team through the stack, the twin, and how bounded agents stay grounded and auditable.