digiLab offers the Uncertainty Engine, an AI platform that integrates probabilistic machine learning and uncertainty quantification into model training, delivering predictions with calibrated confidence intervals. The platform provides automated surrogate‑model generation, explainable AI dashboards, and sovereign deployment options for safety‑critical sectors such as energy, water, and cybersecurity.
Funding
$2.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations and governments that must make high‑stakes decisions often rely on data that is noisy, sparse, or incomplete, making traditional AI models unreliable and increasing operational risk.
Solution
digiLab delivers the Uncertainty Engine, an AI platform that embeds probabilistic machine‑learning and uncertainty quantification directly into the model‑training pipeline. The system generates predictive outputs together with calibrated confidence intervals, enabling users to assess risk quantitatively. Built‑in explainability tools surface the drivers behind each prediction, supporting regulatory compliance and stakeholder trust. The platform supports end‑to‑end workflows—from data ingestion and surrogate‑model generation to cloud‑hosted analytics and API‑based deployment—so teams can move from prototype to production rapidly. By providing a sovereign AI environment, customers retain full control over model versions, data residency, and deployment configurations.
Target Audience
Primary customers are large enterprises, utilities, and government agencies operating in safety‑critical sectors such as energy (fusion, renewable grids), water management, critical infrastructure, and cybersecurity, as well as research labs that require high‑confidence predictive analytics.
Features
- Probabilistic deep‑learning framework that produces predictive distributions and explicit uncertainty metrics for each inference
- Automated surrogate‑model generation for high‑fidelity simulations (e.g., plasma turbulence, fluid dynamics) reducing compute costs by up to 90%
- Explainable AI dashboard with feature‑importance visualizations, counterfactual analysis, and confidence‑interval reporting
- FHIR‑compatible and RESTful APIs for seamless integration with existing enterprise data pipelines and decision‑support systems
- Sovereign deployment options (on‑prem, private cloud, or hybrid) with role‑based access control and end‑to‑end encryption
- Domain‑specific modules for energy systems, water utilities, cybersecurity, and environmental monitoring, pre‑trained on sector‑relevant datasets
- Scalable orchestration layer that automates model versioning, continuous training, and automated rollback in safety‑critical environments