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
June 25, 2025
#12 AI Data Quality: Crap in – Crap out
AI Data Quality – Any AI project is based on data used to train the model. Unlike what we would imagine, getting the right data in the right shape is far from easy or obvious. Building a quality dataset is an engineering work. This paper covers the various steps of this job.
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.
Our latest Publications
June 25, 2025
#12 AI Data Quality: Crap in – Crap out
AI Data Quality – Any AI project is based on data used to train the model. Unlike what we would imagine, getting the right data in the right shape is far from easy or obvious. Building a quality dataset is an engineering work. This paper covers the various steps of this job.
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.