AI Services
AI Software Services
Assessment
• Business analysis • Feasibility assessment • Strategy and Roadmap design • Technical analysis and advisory
Data Services
• Data Acquisition • Data Entry • Data Annotation • Data Cleansing • Data Enrichment
Model Service
• Modelling • Customization & Optimization • Evaluation • Proof of Concept (PoC)
Deployment Services
• Integration • Deployment (Edge & Cloud) • Monitoring, Maintenance & Support
Service Description
The customer has typically identified areas of improved productivity by the means of using AI and Machine Learning.
GoWit-Hungary will take its customer on the AI journey, from problem classification to deployment and maintenance.
How it works
- We analyse the business problem and convert it into a class of Machine Learning problem.
- We identify the data sources that can be used to feed the model, coming from public sources or company assets.
- We train the model with the gathered data and fine tune the "data features" to reach the objectives.
- When stabilised, the model is evaluated et if satisfactory deployed into production.
- We monitor the model and keep feeding it with relevant data while in production to ensure the model does not diverge away from it objectives.
Why Us?
- Experience implementing AI on customers' products.
- GoWit is providing constant supervision of the resources with its management team always accessible to the customer. Decisions regarding your project can be made quickly and efficiently.
- We have 20 years of experience in IT services.
- We can provide both the AI experts and the software development team who will develop the application that will use your AI backend.
Stack
- Cloud and Web Application
- AWS
- Google cloud
- Python
- Java
- Tensor Flow, Pytorch, Keras, etc.
- AWS Sagemaker, AzureML, Google ML
- Any other major technology
Pricing
- Daily rate per resource, based on geo-location and skills
- Time and Material
Our latest Publications
March 25, 2025
#9 AI Training & Back Propagation
AI Training & Back Propagation – In order to use a Digital Neural Network, we need to train it. In this paper we present how we can “train” one using supervised training and backpropagation. By comparing the model’s output with the value that we know to be correct, we can tune the parameters and make it solve the problem at hand.
March 19, 2025
#8 – AI Forward Propagation
AI Forward Propagation – AI Neural networks mimic the neural network of the brain. In this paper we present what is happening inside a digital neural network from data entry to result. We study the various mathematical steps in their simplest format to allow global understanding of the inside mechanisms. The end-to-end process is called Forward Propagation.
March 10, 2025
#7 – Artificial Intelligence : Architecture: Neural Network Design
Artificial Intelligence : Architecture: Neural Network Design – AI Neural networks mimic the neural network of the brain. Once the technical architecture has been built, how does each component work? We present the various mathematical component in action.
Our latest Publications
March 25, 2025
#9 AI Training & Back Propagation
AI Training & Back Propagation – In order to use a Digital Neural Network, we need to train it. In this paper we present how we can “train” one using supervised training and backpropagation. By comparing the model’s output with the value that we know to be correct, we can tune the parameters and make it solve the problem at hand.
March 19, 2025
#8 – AI Forward Propagation
AI Forward Propagation – AI Neural networks mimic the neural network of the brain. In this paper we present what is happening inside a digital neural network from data entry to result. We study the various mathematical steps in their simplest format to allow global understanding of the inside mechanisms. The end-to-end process is called Forward Propagation.
March 10, 2025
#7 – Artificial Intelligence : Architecture: Neural Network Design
Artificial Intelligence : Architecture: Neural Network Design – AI Neural networks mimic the neural network of the brain. Once the technical architecture has been built, how does each component work? We present the various mathematical component in action.