Signaloid provides a cloud-based computing platform that utilizes deterministic computation on probability distributions to enhance uncertainty quantification in AI models and Monte Carlo simulations. This technology allows users to run existing C/C++ code with minimal modifications, significantly improving the speed and accuracy of risk assessments in quantitative finance and engineering applications.
Funding
$4.2M 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
Uncertainty quantification in AI models, quantitative finance, and engineering simulations often relies on computationally intensive Monte Carlo methods, requiring extensive resources and time. Traditional computing approaches struggle to efficiently handle the probabilistic nature of these calculations, leading to slow risk assessments and pricing calculations.
Solution
Signaloid offers a computing platform that utilizes deterministic computation on probability distributions to accelerate uncertainty quantification. This platform allows users to run existing C/C++ code with minimal modifications, enabling faster and more accurate risk assessments. Signaloid's technology transforms probability distributions into regular data, streamlining the computation process and improving efficiency. The platform is available through a cloud-based task execution API, integration with on-premises code, or edge hardware modules.
Target Audience
The primary users are developers and organizations in quantitative finance, AI, and engineering who require efficient and accurate uncertainty quantification in their models and simulations.
Features
- Cloud-based REST APIs for executing C/C++ kernels with automatic support for deterministic computation on probability distributions.
- Seamless integration with existing CPU- or GPU-optimized code.
- Edge hardware modules for energy-efficient real-time uncertainty quantification.
- Ability to augment or reuse existing code, or build new applications.
- Deterministic computation on probability distributions associated with all in-processor state.