A model trained on your data knows your business, whereas one off the shelf only knows the average.
As a data science consulting firm, we build AI models and apply advanced data science techniques that turn your operational data into a concrete, measurable advantage. Regardless of industry. Regardless of data source.
Forecast before the event happens.
Before orders drop, before a customer churns, before a machine fails. You have time to react, not just a report after the fact. We match the method to the problem: LSTM, Gradient Boosting, survival models.
Patterns hidden in operational data.
Your operational data holds answers your BI does not show. Where you lose margin, what drives sales, which customer segments behave differently. We bring it to the surface.
Unstructured data speaks too.
Reviews, comments, service tickets, documents. Where data is text, there are signals that cannot be processed manually. We turn them into measurable insights.
We work with what you have.
No need to migrate data or build a new stack. We work with what you have: SAP, ERP, Snowflake, your own data warehouse, CSV files. The model fits your environment, not the other way around.
AI platforms and SaaS tools solve general problems. Your problem is specific. Your data is unique. Your patterns are irreplaceable.
A model trained on your data knows patterns no off-the-shelf software can see. It knows when your customers order, how they respond to seasonality, where anomalies arise. A competitor cannot buy that.
SaaS costs more with every year and every user. Your model becomes more accurate with every week of new data. After a year it knows your business better than the best analyst.
You see a result first: we work on your real data and show, in a model calculation, what that means in EUR or USD. Only then do you decide on full deployment.
Generic, not yours
SaaS knows as much about your business as it does about your competitor's. No off-the-shelf tool knows your patterns.
No competitive moat
Anyone who pays for a licence gets exactly the same tool as you. A custom model is IP that nobody can copy.
Ongoing cost with no IP
When you stop paying the subscription, you lose everything. A custom model stays yours and grows in value.
From data audit to production monitoring. We work side by side with your team or independently, if you prefer a ready-to-use solution.
We do not use LLMs where XGBoost is enough. We do not train a neural network where a linear model gives a better result faster. Architecture follows data and the problem, not hype.
Data audit, feature engineering, exploration, training, validation, deployment, drift monitoring, retraining. Every stage with documentation.
SAP, Snowflake, BigQuery, PostgreSQL, flat files, REST API, Kafka. We integrate the model with what you have. No migration to a new stack required.
We deliver production-ready models, technical documentation, and knowledge transfer. Your team can integrate, monitor, and develop them independently. Zero vendor lock-in.
Data audit and assessment
We check quality, completeness, and data potential before starting modelling.
Model design
Algorithm selection, feature engineering, validation and backtest setup.
Deployment and monitoring
Production deployment, automatic retraining, alerts on model drift.
Documentation and knowledge transfer
Full technical documentation and team training on operating and extending the model.
We combine over 30 years of software engineering experience with certification as Applied AI and Data Science Professionals at MIT Professional Education and as Deep Learning Specialist at Bitkom Akademie in Germany.
Established ML and data science techniques paired with deep learning, applied specifically to your problem.
We speak the language of business and understand data. Results are measured in EUR and USD, not in model metrics.
We show value before the decision is made. Pilot on real data, a model calculation in EUR, then deployment.
An effective AI model requires a thorough understanding of the data, the business problem, and the environment it will operate in.
That is why we start with a data workshop, build a pilot on real data, show value in a model calculation and only then move to full deployment.
Guido Winger
Co-Founder
Guido Winger is a software engineer and co-founder of myBytes. His earlier engineering history includes the development and management of the e-commerce platform xt:Commerce.
This was followed by formal data science training: certification as an Applied AI and Data Science Professional (MIT Professional Education) and as a Deep Learning Specialist (Bitkom Akademie). Today he translates the business problems of mid-sized companies into AI models that run in production, from the data audit to the business case in EUR.
Mariusz Pianowski
Co-Founder
Mariusz Pianowski has been developing and scaling production systems for more than 20 years. Like his co-founder, he is a certified Applied AI and Data Science Professional (MIT Professional Education) and Deep Learning Specialist (Bitkom Akademie). His focus: integrating AI models into existing architectures, whether SAP, ERP or your own data warehouse. That requires neither a migration nor a new stack.
Only 26% of German companies use AI so far, so start now and you are ahead. EU AI Act from August 2026. 23 pages of market data, recommendations and forecasts for mid-sized companies.
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