Machine Learning and Predictive Analytics
Models that forecast, score and detect, built on your data and explained to the people who use them.
Why It Matters
Pricing, credit, stock and fraud decisions are made many times a day. Static rules and spreadsheet models miss patterns in the data and react late when the market moves.
A model that cannot explain itself is hard to defend to a customer, an auditor or a credit committee. One that is never watched drifts away from the business it was built for. The common failures are a model with no clear decision to support, training data that hides a bias, and no owner after launch.
What We Do
Forecasting, Pricing and Demand
Models that predict demand and price from your own records, so stock, capacity and margin are planned on evidence.
Credit and Risk Scoring With Explainable AI
Credit and risk scores with the factors behind each one, ready for customers, credit committees and auditors.
Fraud and Anomaly Detection
Unusual transactions are flagged as they happen, with the reasons attached and a queue for your team.
Recommendations
Suggestions for each customer, tested against what they actually bought and measured after release.
Simulation Models
Models that test a pricing, stock or capacity decision before you take it in the real world.
What Changes for You
Decisions From Your Data
Forecasts, prices and scores come from your own records and update as those records change.
Risk Caught in Time
Fraud and unusual behaviour are flagged as transactions happen, with the reasons attached.
Decisions You Can Explain
Every score comes with the factors that drove it, in words a customer, auditor or committee can follow.
How the Work Runs
Every model starts from a decision and a cost of being wrong. Your team sees the comparison with the current method before any model is accepted.
Define the Decision
We agree the decision the model supports, how success is measured and what a wrong answer costs. You name the people who will act on the result.
Prepare the Data
We assess the data you hold, build the features and check for gaps and bias. Your data owners confirm what may be used and how long it is kept.
Beat the Baseline
We compare candidate models with your current method and keep the simplest one that clearly does better. You see the comparison before any model is accepted.
Explain and Validate
We test on unseen data, explain the decisions and document the model for your risk and audit teams. They review it before release.
Deploy and Monitor
We connect the model to your systems and watch for drift. You set the threshold that triggers a review, and we retrain through a tested pipeline.
Retrain on a Schedule
We retrain on a schedule you set, compare the new model with the live one and keep the version that performs better.
Platforms and Technologies
The work uses the machine-learning tools your engineers already know. Google Cloud and Amazon Web Services provide the compute, and NVIDIA GPUs are the processors that run the models. PyTorch and TensorFlow are the main libraries for training a model. The scikit-learn library covers the smaller statistical models, and XGBoost is a fast method for data held in tables. MLflow records each experiment, its data and its result, so a model can be rebuilt and compared.
- AI and machine learning
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- MLflow
- Methods We Use
- Forecasting and demand planning
- Models that show their reasons
- Fraud and unusual activity, as it happens
- Scenario simulation
Naming a product does not mean a partnership with its vendor.
NVIDIA
We size and build GPU estates on NVIDIA for training and inference, then prove the stack under a real workload.
Google Cloud
We build data, analytics and AI systems on Google Cloud, with BigQuery, Vertex AI and GKE under one control model.
AWS
We set up AWS accounts and guardrails, move workloads with its migration tools and keep cost and access visible.
Industries
Banking and Financial Services
Banking, payments, lending, wealth, insurance and digital wallets: the platforms they run on and the connections between them.
Energy and Utilities
Smart-grid analytics, OT defence, asset maintenance and ESG reporting for energy producers, utilities and grid operators.
Logistics and Distribution
Warehouse, transport and carrier integrations, tracking and proof of delivery, joined to your ERP and finance.
Manufacturing and Enterprise ERP
ERP extensions, MES and supplier integrations, supplier file feeds and planning data for manufacturers and multi-entity enterprises.
Media and Telecommunications
Consumer platforms, streaming, connected devices and ad-tech that stay fast at peak audience, with AI for content and personalisation.
Retail and Omnichannel Commerce
POS integration, stock and price sync, card payments, loyalty and e-commerce for retailers with many sites.
Questions
What buyers ask before a model is put into a live decision.
How much data do we need?
It depends on the decision and how much the data varies. We assess what you hold at the start and tell you plainly if it is enough. Where it is not, we say what to collect or whether a simpler method will do.
Can you explain a decision to an auditor or a customer?
Yes. We use methods that show which factors drove each decision, and we document how the model was built and tested. Your risk and audit teams review it before release.
How do you check for bias?
We test how the model performs across the customer groups that matter to you. Where results differ without a sound reason, we change the data, the features or the model before release.
What happens when the model stops performing?
We monitor performance and the data the model sees, and alert your team when either shifts. You decide when to retrain, and the new model is tested against the old one before it replaces it.
Who owns the model and the features?
You own the code, the features, the trained model and the documentation we produce for you. Data stays yours throughout, and third-party licences are listed in the handover.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.