Machine learning development

Models that predict, classify and flag what people would miss. We start from the decision you want to improve, check that your data can support it, and measure the model against a simple baseline before anyone relies on it.

What’s included

Demand and sales forecasting

By product, location and week, with a range instead of a single magic number.

Churn prediction and segments

Which customers are likely to leave, and what they have in common.

Recommendations and pricing

What to show each customer, and prices that respond to demand.

Fraud and anomaly detection

Unusual orders, payments or readings flagged for a person to review.

Reproducible work

Versioned data, tests and a clean record of every experiment.

When it makes sense

  • You plan stock or staff on gut feel and old spreadsheets.
  • You lose customers without seeing it coming.
  • Odd transactions are found weeks later.

What you get

  • A check on your data before the build
  • A model compared against a simple baseline
  • An API or scheduled job that serves predictions
  • Accuracy monitoring after launch

Tools we work with

We pick what fits your project and your team, and we’ll say why.

  • Python
  • scikit-learn
  • PyTorch
  • XGBoost
  • pandas
  • MLflow

Related work

Built by our engineers before Orandic. None of these clients hired Orandic.

Machine learning

Native language from spoken English

Hears five seconds of English and predicts the speaker’s first language: Hindi, Kannada, Malayalam, Tamil or Telugu.

Harsh Sahu’s machine-learning project. The code and the experiment log are public.

$ python -m pytest$ ruff check src scripts tests dashboard

Data science

Falcon 9 landing prediction

An end-to-end data-science project done properly: reproducible data with checksums, leakage-safe model selection, a stricter time-based test, automated tests on every push and an interactive dashboard.

Harsh Sahu’s data-science project.

Questions about machine learning

Do we have enough data?

Often more than you think, but not always. We check first, and if your data can’t support a reliable model, we tell you before you spend money building one.

How accurate will it be?

We can’t promise a number before seeing your data. We measure every model on data it hasn’t seen, compare it with a simple baseline, and only ship it if it clearly beats that baseline.

Related services

Tell us what you need built.

A free 30-minute call with an engineer. You get the scope, timeline and price in writing before you commit, and a reply within one working day.