About
PRAESC GmbH
We exist to close the gap between AI research and industrial deployment — building Physical AI systems that run reliably on embedded hardware in production.
"After a decade delivering edge AI projects, I kept seeing the same pattern: strong research, weak deployment. The gap between a prototype that works in the lab and a system that runs reliably on industrial hardware is where most projects fail — and where PRAESC operates."
— Dr. Miguel de Prado, Founder & Managing Director
LinkedIn →Vision
AI capabilities are no longer the bottleneck. The bottleneck is deployment — getting models that work in a lab to run reliably, safely, and autonomously on industrial hardware in the field.
We believe the next decade of industrial AI belongs to firms that can close this gap: building systems grounded in physical principles, validated on real hardware, and designed for operational sovereignty. PRAESC is built around that conviction.
Principles
- ▲ Edge-native. Inference runs on-device. No cloud, no connectivity constraint, no IP leakage. Operational sovereignty is not optional.
- ▲ First principles over black boxes. Bayesian reasoning, physical models, and calibrated uncertainty built into every system. We build AI that knows what it doesn't know.
- ▲ Trustworthy by design. Confidence gates, human-in-the-loop, and EU AI Act alignment are architectural decisions — not afterthoughts.
Capability stack
PRAESC covers the full Physical AI stack — from algorithm design and Bayesian inference to embedded hardware optimisation and industrial deployment.
You engage directly with the technical lead on every project. Domain specialists join specific engagements as needed — always vetted and under NDA. Every project produces full documentation and reproducible deployment artefacts so your team can operate the system independently.
Physical AI & Edge Deployment
Bayesian Inference, VLA fine-tuning, model compression, TensorRT/ONNX optimisation — deployed on embedded targets.
Bayesian Inference & Control
Step filter implementations, uncertainty quantification, confidence-gated execution, CUDA-accelerated inference kernels.
Robotics Systems Engineering
Full perception-to-actuation pipeline: ROS2 integration, kinematics and manipulation planning, sensor fusion and calibration.
Embedded Hardware Optimisation
Hardware-aware model design for power and latency constraints. Real-time deployment on Jetson, NXP, Qualcomm, RPI, and STM32.
Simulation & Digital Twin Development
Full digital-twin development in Isaac Sim before any real hardware. Sim-to-real gap closed iteratively — de-risking deployments and accelerating on-site validation.
Industrial AI Integration
MES/SCADA interfacing, digital twin development, production-line validation, and handover-ready technical documentation.
Company
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