Machine learning & computer vision

Machine learning development services

Not every AI problem needs a chatbot. We build machine learning models that predict, classify, recommend and see, trained on your own data and deployed where they create value.

All services
  • Python
  • scikit-learn
  • PyTorch
  • XGBoost
  • ML Kit
  • TensorFlow Lite

Overview

Machine Learning, explained.

Machine learning (ML) is a branch of AI where models learn patterns from historical data to make predictions or decisions on new data. It powers demand forecasts, fraud and risk scores, recommendations, churn prediction and computer vision tasks such as reading text from images.

For many business problems a well-built ML model is cheaper, faster and more accurate than a large language model. The work is mostly about data: cleaning it, choosing the right features, validating honestly so results hold up in the real world, and monitoring the model as conditions change.

theBlume handles the full lifecycle, from data preparation and model training to deployment as an API, inside a dashboard or on-device in a mobile app, plus the monitoring that keeps it accurate.

What we deliver

Machine Learning services we deliver.

  • Predictive analytics & forecasting

    Demand, revenue, inventory and capacity forecasts that planning teams can act on.

  • Classification & risk scoring

    Models that flag fraud, credit risk, churn or priority so people focus on the cases that matter.

  • Recommendation systems

    Personalised product, content and course recommendations driven by behaviour and context.

  • Computer vision & OCR

    Text recognition from receipts, invoices and forms, plus image classification and quality checks.

  • On-device machine learning

    Models that run inside Android and iOS apps for privacy, offline use and instant results.

  • MLOps & monitoring

    Training pipelines, versioned models, drift detection and retraining so accuracy doesn’t silently decay.

Use cases

Where it creates real value.

  • Retail & e-commerce

    Demand forecasting per product and location, plus recommendations that lift basket size.

  • FinTech

    Transaction categorisation and anomaly detection that send only suspicious activity to reviewers.

  • Manufacturing

    Predictive maintenance from sensor data and visual inspection of products on the line.

  • Education

    Early-warning models that identify learners at risk of dropping out so mentors can step in.

How we deliver

A clear path from idea to launch.

  1. Frame the prediction

    We define exactly what to predict, how it will be used, and which errors are costly, before touching a model.

  2. Prepare the data

    Data is collected, cleaned, labelled where necessary and split so validation reflects real-world use.

  3. Train and validate

    We compare simple baselines with stronger models and only ship what beats the baseline on held-out data.

  4. Deploy and monitor

    Models go live as APIs, in dashboards or on-device, with drift monitoring and a retraining plan.

What you receive

  • Data assessment and baseline results
  • Trained, validated model with documented accuracy
  • Model API, dashboard or on-device package
  • Drift monitoring and alerting
  • Retraining pipeline and model registry

Technology

Tools we build with.

  • Python
  • scikit-learn
  • PyTorch
  • XGBoost
  • ML Kit
  • TensorFlow Lite
  • SQL
  • Feature pipelines
  • Model registry
  • Cloud ML platforms

Computer vision running in our own app

Fairly, our shared-expense app, uses on-device text recognition to read a receipt and pre-fill each line item for splitting, with nothing sent to a cloud vision service. It works abroad and offline, and keeps receipts private.

See our products

Machine Learning FAQ

Common questions.

Answers to what clients ask most about our machine learning services.

Machine learning development is building models that learn from historical data to make predictions or decisions, such as forecasts, risk scores, recommendations or recognising text in images, and deploying them so they can be used by people or software.

It depends on the problem. Forecasting and classification often work with a few thousand good historical records; computer vision may need labelled examples or can start from pre-trained models. We assess your data in discovery before promising results.

Use machine learning for predictions from structured data, such as numbers, categories and history. Use a language model for understanding and generating text. Many solutions combine both, and we recommend whichever is more accurate and cost-effective for the task.

Yes. We deploy models on-device in Android and iOS apps when privacy, offline access or instant response matters, as we did for receipt recognition in Fairly.

We agree a target metric with you, such as forecast error or precision on flagged cases, compare the model against a simple baseline, and test it on recent data it has never seen before recommending it for production.

Related

Related services

Let’s talk

Let’s make your idea bloom.

Tell us what you’re building. We’ll get back to you with clear next steps.