Automate & Optimize your business with AI
From multiobjective hyperparameter tuning for Machine Learning Models to complex Digital Twins:
paretos brings together the power of an AI supported algorithm to deal with high-dimensional, complex models, easy to integrate via modular, API-based services and a comprehensive data analytics solution and visualization.
Boost Complex Models
Boost the ability of your predictions, recommendation engines and neural nets. After investing a lot of time and money into the development of models most companies use them manually or only with static grid search methods. This can be done better!
Automate ML Algorithms
Automate & optimize your machine learning algorithms, neural nets and model quality. Find customized, optimal algorithms with paretos’ A.I. based Optimization Engine.
Optimize Smart Grids
Optimize your Smart Grid Systems i.e. fleet charging, ancillary services and home energy storage. Find customized, optimal designs and scalable operational strategies with paretos’ A.I. based Optimization Engine.
the potential of Machine Learning Models or Digital Twins with paretos optimization algorithm “Socrates“, integrated smoothly via our API
the full technological potential and visualize it with paretos Data Analytics solution “paretOS“
strategic decisions by paretos Decision Management System “DMS” and benefit from complex mathematical optimization “as a Service”
paretos optimization algorithm Socrates has been validated in complex projects, such as the automated development of neural nets for bee recognition, development of an Electric Vehicle or the design of hybrid powertrains. They serve as a showcase of the significant increase in efficiency.
System time reduction
through integrated automation
With full factorial observation
Neural net performance boost
for fast and precise bee recognition
through efficient optimization
compared to full factorial evaluation
where each training run takes up to several days
by showing best train alternatives for a specific track
through multi objective optimization engine Socrates
Compareted to full factorial observation
where one train simulation takes one minute
What others say
paretos AI-based Optimization Engine can be integrated quickly, easily and securely via an API, while the raw and model data is kept on the customer’s server. Our Optimization Engine only requires the hyper parameters, the meta performance and the optimization metrics.
By a dockerized model layer and a lean meta data interface on the client side, paretos is able to provide the AI-based optimization via an API. Finally, the paretos optimization engine triggers the model optimization fully automatically and highly efficiently, receives all optimal results of a technology potential of a simulation or a Hyperparameter Tuning for Machine Learning Models.
In addition we provide data analytics and visualizations based on the learning of the model optimization.
Beside the fact, that with paretos you are able to integrate AI Optimization to your business as easy as to connect to a new mail-service, our Optimization Algorithm was researched over several years at the University of Applied Sciences Munich and has been recognized and validated worldwide (e.g. by the MIT). It is now being further developed and transferred to the application by paretos and outperforms state-of-the-art optimization Algorithms in high-dimensional spaces. Feel free to get in touch with us for mindblowing benchmarks!
paretos optimization engine works with every model and model management tool. It can be used for Machine Learning models, design (hyperparameter tuning) of neural nets (e.g. for prediction or object recognition) or for virtual prototypes, Digital Twins and other complex simulations.
But beside of that: Get in touch with us today to find out how we can integrate AI based Optimization in your company or for further use cases and success stories!
As we provide our Optimization Engine via API you can connect via API calls or via all common model management tools (such as TensorFlow, PyTorch, SageMaker, Matlab, Modelica). The raw and model data is kept on the customer’s server. Our optimization engine only requires the hyper parameters, the meta performance and the optimization metrics. By a dockerized model layer and a lean meta data interface our Optimization Engine triggers the model optimization fully automatically and highly efficiently.
As we provide our Optimization as a Service (per API calls) we charge our clients by a monthly subscription fee or a “pay per use” fee. The set-up costs and effort is very low, as we can communicate with all common tools and languages (such as TensorFlow, PyTorch, SageMaker, Matlab, Modelica)
Fabian is fascinated by the potential mathematical optimizations for the design of complex problems. Raised as an engineer at a huge german OEM, he went into Systems Engineering and maths – his PhD is about “Efficient multi objective optimization for expensive high-dimensional blackbox problems”. He co-founded paretos with the urge of closing the gap between maths and business applications.
Thorsten is fascinated by the potential mathematical optimization can bring to complex (real-life) problems. With focus on organizational & product growth, he co-founded companies, worked as a management coach and recently was COO with moovel/REACH NOW before co-founding paretos. His motivation is to provide AI-based technology for “everyone” to make better strategic decisions.
paretos "in a nutshell"
paretos automates and optimizes virtual prototypes, AI/ML models or complex simulations with a multi-layered, AI-supported algorithm and thus enables companies to use state-of-the-art optimization methods in development and decision-making processes. The software and the optimization algorithm can be easily integrated into the existing IT landscape, raising new technology potentials and leading to dramatic efficiencies.
The AI-based optimization algorithm was researched over several years at the University of Applied Sciences Munich and has been recognized and validated worldwide (e.g. by MIT). It is now being further developed and transferred to the application by paretos.