digiLab

Founded 2021

Build machine learning models with uncertainty quantification for engineering and scientific simulations

Pricing model

Demo

You can use digiLab to build surrogate models from complex physical simulations and evaluate uncertainty in critical technical operations. The system processes sparse, incomplete, or noisy datasets across engineering, energy, and water management sectors to guide high-stakes decision making. You can deploy explainable machine learning models, analyze plasma turbulence, forecast infrastructure failures, and assess environmental metrics without needing a deep data science background. Teams can create explainable workflows that assess risk bounds and test physical systems accurately before executing costly real-world operational changes.

What digiLab does

Upload simulation data to generate fast surrogate modelsCalculate uncertainty boundaries for physical test parametersTrain probabilistic models using sparse or noisy sensor dataDeploy explainable decision workflows for critical industrial assetsForecast failure risks across complex mechanical systemsReview confidence scores for automated engineering recommendationsQuantifies uncertainty in sparse or incomplete technical datasetsBuilds surrogate machine learning models from heavy physical simulationsProduces explainable predictions for safety-critical engineering tasksOperates without requiring custom data science scripting from usersSupports sovereign AI deployments to maintain private model controlIntegrates sensor data to calculate efficiency and risk metrics

Pricing breakdown

Pricing model: Book a demo

Frequently asked

Python (Primary SDK/API access), Jupyter Notebooks (Research environment), MATLAB (Engineering simulation), Docker (Containerized deployment), REST API (Custom software hooks), AWS (Cloud infrastructure), CSV/Parquet (Scientific data formats), Python SDK, Jupyter Notebooks, MATLAB, Docker, AWS S3, REST API, CSV/Parquet, PyTorch/TensorFlow

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