Machine learning development.

At Meybrain Technologies we first test whether your data can support a useful model, then build it and connect it to the software you use every day. We design the model as part of a real decision, not as a stand-alone experiment.

Machine learning solves a different problem from language models. Language models work with text; machine learning models mostly learn from tabular data such as sales, sensor readings, transactions or stock levels. They answer questions like "how much of this product will we sell next month?" or "is this transaction unusual?".

• 01

  Demand forecasting

  By product, branch or region.

• 02

  Churn prediction

  Spotting customers likely to leave.

• 03

  Anomaly detection

  Unusual transactions, production defects or sensor drift.

• 04

  Classification

  Of documents, requests or images.

• 05

  Recommendations

  Ranking products or content for each user.

• 06

  Predictive maintenance

  From machine data.

Machine learning makes sense when two things are true: you have consistently recorded historical data, and a decision based on it is made often. Retail, e-commerce, manufacturing, logistics, energy and finance companies often meet both. If your data is scattered or the decision is rare, starting with data analytics and reporting may be wiser, and we will tell you so.

• 01

  Data readiness report

  What data exists, its gaps, quality and suitability.

• 02

  A simple baseline

  Today's method or a simple rule, to show whether a model really adds value.

• 03

  Experiments and evaluation report

  In business terms: not just "what is the accuracy?" but "how many bad orders does it prevent, and how many false alarms does it raise?".

• 04

  Model service

  An API other systems can call.

• 05

  Integration

  Into the screen, report or workflow where the prediction is used.

• 06

  Monitoring

  For data and performance drift.

• 07

  Retraining plan and documentation

1. 01

   Frame the problem

   Which decision, how often, and what an error costs.

2. 02

   Audit the data

   Sources, length of history, consistency.

3. 03

   Set a baseline

   Measure how good the simple approach already is.

4. 04

   Experiment

   Compare methods on the same held-out data.

5. 05

   Business review

   Interpret results with you in terms of impact. If the model does not beat the baseline, we say so and recommend stopping.

6. 06

   Deploy

   Wrap the model as a service and connect it.

7. 07

   Monitor and refresh

   Track performance as conditions change and retrain when needed.

We work in the Python ecosystem. For tabular data, scikit-learn and tree-based methods such as XGBoost or LightGBM are often enough, and easier to explain. For images, audio or very large datasets we move to deep learning with PyTorch. Not every problem needs deep learning; a simpler, explainable model is often the better choice because it is easier to operate.

We track experiments with tools such as MLflow and serve models through a containerised API. The model can run where the data lives, on your servers or in your cloud account.

İmarsal, our own product in launch preparation, turns structured data into a decision output. Starting from a block and parcel number, it looks up zoning conditions, calculates floor-area ratios and produces an AI-assisted feasibility assessment, so owners, contractors, architects and advisers can decide from the same output. See it in our portfolio.

The same principle guides our machine learning work: a model creates value inside a decision someone actually makes.

We split machine learning work into stages, so you see what your data promises before a large investment:

• 01

  Data audit and feasibility

  A short, fixed-price stage.

• 02

  Proof of concept

  A fixed-price stage scoped from the feasibility findings.

• 03

  Deployment and monitoring

  Priced by integration scope, with operations support as an option.

Cost depends on data quality and how much labelling is needed, the number of models, real-time versus batch predictions and the integration scope. Compute-heavy training is costed separately.

Each stage is priced after a short call about your data and goal, in a written, itemised quote.