Machine Learning

Models that earn their keep.

Enterprise-grade machine learning, built on AWS. We turn your raw data into models that ship fast, scale cleanly, and move a number the business actually cares about.

Built on AWS·Deploy in days, not months·Scales prototype to production
From data to decision
Predictforecast what’s next
Detectcatch the outliers
Personaliserecommend what fits
What we buildCapabilities

End to end, data to insight.

Six things we do well — each one an application, not a science project.

Predictive analytics

Use history to forecast trends, behaviour, and outcomes — with models you can trust.

Computer vision

Pull meaning from images and video with state-of-the-art vision models.

Natural language

Process, analyse, and generate language — classification, sentiment, and more.

Anomaly detection

Spot unusual patterns and outliers — fraud, failures, and issues before they bite.

Recommendation systems

Personalised recommendations from behaviour, preference, and lookalike patterns.

Reinforcement learning

Self-improving systems that learn optimal behaviour by interacting with their world.

Built on AWSInfrastructure

The full AWS ML stack.

Scalable, secure, cost-effective — we use the right managed service for the job instead of reinventing it.

SageMaker

Build, train, deploy at scale.

Lambda

Serverless, event-driven inference.

S3

Data and model artifacts.

EC2

Elastic compute for training.

Glue

Serverless ETL and prep.

Comprehend

NLP without the heavy lifting.

Rekognition

Image and video analysis.

Forecast

Accurate forecasts, no PhD.

Rapid deployment

Models in production in days, on managed services.

Scalable by default

Prototype to production on elastic infrastructure.

Cost-aware

Pay for what you use; serverless where it fits.

Enterprise security

AWS controls plus our best practices on your data.

How we deliverProcess

Structured, iterative, honest.

01

Data assessment & strategy

We evaluate your data, find the gaps, and set a strategy tied to a real business goal — plus an honest feasibility read.

02

Data engineering & prep

Robust pipelines, clean data, and the feature engineering that quietly decides whether a model works.

03

Model development & training

Custom models matched to your data — algorithm selection, architecture, validation, and tuning.

04

AWS deployment & integration

Deployed on AWS with automation, APIs, monitoring, and logging built in from day one.

05

Monitoring & optimisation

We watch for drift, retrain when needed, and keep the model improving as conditions change.

Start a build

Sitting on data you’re not using?

Tell us the decision you want to get right. We’ll come back with the model, the AWS stack to run it, and a plan to ship it.

What you get
  • A model tied to a real decision
  • Running on AWS, monitored
  • An honest read on feasibility