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
May 20, 2025
#11 AI: Fixing the Training gone Wrong
Building on Paper #10’s AI training pitfalls—underfitting (too lazy), overfitting (too rigid), high bias (skewed guesses), and high variance (wild swings)—this paper offers practical fixes for our smell detector. We explore three levers: boosting network capacity, extending training with more epochs, and enriching data for smarter learning.
April 29, 2025
#10 AI Training going wrong
This paper explores why the model might fail in practice: underfitting (too simplistic), overfitting (too rigid), and the underlying issues of bias and variance. Through examples, we show how underfitting leads to random guesses , while overfitting causes oversensitivity. We introduce bias (consistent errors) and variance (prediction variability).
Our latest Publications
May 20, 2025
#11 AI: Fixing the Training gone Wrong
Building on Paper #10’s AI training pitfalls—underfitting (too lazy), overfitting (too rigid), high bias (skewed guesses), and high variance (wild swings)—this paper offers practical fixes for our smell detector. We explore three levers: boosting network capacity, extending training with more epochs, and enriching data for smarter learning.
April 29, 2025
#10 AI Training going wrong
This paper explores why the model might fail in practice: underfitting (too simplistic), overfitting (too rigid), and the underlying issues of bias and variance. Through examples, we show how underfitting leads to random guesses , while overfitting causes oversensitivity. We introduce bias (consistent errors) and variance (prediction variability).