AI/ML Development Services
Most machine learning projects fail before modelling starts, in the data. Labels that are inconsistent, a target that leaks, or a dataset that does not represent the conditions the model will run in — none of which a better algorithm can fix.
Discuss your ML project
What We Build
Classical machine learning where it beats a language model — which is more often than current fashion suggests.
Forecasting
Demand, revenue, capacity and inventory forecasts with prediction intervals rather than a single misleading number.
Classification & Scoring
Churn, credit, lead and risk scoring with calibrated probabilities and explainable drivers.
Anomaly Detection
Fraud, fault and outlier detection tuned to a false-positive rate your team can actually work through.
Computer Vision
Detection, classification, OCR and quality inspection, deployed to cloud or edge devices.
Recommendation
Ranking and personalisation trained on interaction data, with cold-start and diversity handled.
MLOps
Pipelines, model registry, deployment and monitoring, so a model is a maintained asset rather than a one-off.
What We Deliver
Reproducible pipelines
Training that can be rerun end to end and produce the same result.
Explainability
Feature importance and per-prediction explanations, which most regulated use cases require.
Serving infrastructure
Batch or real-time inference sized to your latency and volume requirements.
Monitoring
Data drift, prediction drift and performance tracked against ground truth as it arrives.
Fairness checks
Performance across segments measured, so a model is not quietly worse for part of your users.
Model documentation
What it does, what it was trained on, its limits and its known failure modes — written down.
Where We Put AI To Work
We take on AI projects where the outcome can be measured — a cost that falls, a queue that clears, a decision that gets more accurate.
Talk to our teamAdvanced AI Engineering Capabilities
The difference between a demo that impresses and a system you can rely on.
Evaluation harness
A scored test set built from your real data, run on every prompt, model or retrieval change.
Guardrails
Input and output filtering, prompt-injection defences and refusal behaviour you have chosen.
Cost engineering
Caching, model tiering and context trimming so unit economics work at real volume.
Provider abstraction
One interface across providers, so switching model is configuration rather than a rewrite.
Self-hosted options
Open-weight models served in your own infrastructure where data cannot leave your estate.
Full request logging
Prompt, context, response and score retained for debugging and audit.
How We Work
Data assessment, baseline, then improvement that has to prove itself.
Data assessment
Volume, quality, labels and leakage examined, with an honest go or no-go.
Baseline model
The simplest thing that could work, deployed early to establish the real number.
Improve
Feature engineering and model selection, measured against the business metric.
Deploy and monitor
Serving infrastructure, monitoring and a retraining plan.
Find Out What Your Data Can Do
Start with a data assessment. You will get a written verdict on feasibility, and we will tell you if the answer is no.
Request a data assessmentML Questions
Everything you need to know about our ai development work.