Service

AI & Machine Learning

Predictive models, computer vision, and NLP built for production.

PythonPyTorchTensorFlowMLflowAWS SageMaker

Overview

What we deliver

We add machine learning to products: recommendations, forecasting, computer vision and text classification. The hard part is rarely the model. It's clean data, monitoring and retraining, and that's where we spend our effort. Our ML engineers work across the full lifecycle from data engineering through to deployed, monitored models.

What's Included

Key capabilities

ML Model Development

Classification, regression, clustering, NLP, and computer vision models built and validated for your dataset.

Data Pipeline Engineering

ETL pipelines, feature stores and data quality checks, because models are only as good as their data.

Experiment Tracking

Reproducible experiments with tracked hyperparameters, metrics, and artefacts using MLflow and W&B.

MLOps & Deployment

Models deployed as APIs, monitored for drift, and retrained automatically when performance degrades.

Recommendation Systems

Recommenders trained on how your users actually behave.

Computer Vision

Object detection, image classification, OCR, and anomaly detection for images and video streams.

Technology

Our technology stack

ML Frameworks

PyTorchTensorFlowScikit-learnXGBoostHugging Face

MLOps

MLflowWeights & BiasesKubeflowMetaflowDVC

Data Engineering

Apache SparkAirflowdbtPandasPolars

Cloud ML

AWS SageMakerGoogle Vertex AIAzure Machine Learning

Infrastructure

DockerKubernetesNVIDIA CUDARay

Who It's For

Common use cases

Recommendation engines for e-commerce
Predictive maintenance for manufacturing
Fraud detection for fintech
Churn prediction for SaaS
Demand forecasting for logistics
Medical image analysis for healthcare

Not sure if this is right for you?

Talk to an engineer first.

We offer a free 30-minute discovery call to understand your problem and tell you honestly whether we're the right fit. No sales pitch.

Book a Discovery Call →

FAQ

Frequently asked questions

Do we need a large dataset to start?

Not always. It depends on the problem. We look at your data first. Transfer learning or a pre-trained model often means you need far less than you'd think. We won't start a project we don't think can succeed.

How do you deploy models to production?

As REST APIs on containerised infrastructure, integrated into your existing backend. We include monitoring for data drift and model degradation, with automated retraining pipelines.

How is this different from your Gen AI service?

Gen AI covers large language models and foundation models (GPT-4, Claude, RAG). This service covers models you train on your own data, such as forecasting, computer vision and text classifiers, rather than prompting a pre-trained model.

Also