Machine Learning & MLOps
From research to revenue: ML systems that deliver measurable business outcomes. We build, deploy, and operate machine learning at enterprise scale.
Schedule a ConsultationEnd-to-end ML capabilities from experimentation to production, with the operational rigour enterprises demand.
From classical machine learning to deep learning and LLMs, we develop models that are optimised for your specific data, latency requirements, and accuracy targets.
Automated training, deployment, and monitoring pipelines. Drift detection, performance dashboards, and retraining triggers that keep models reliable in production.
Centralised feature management that eliminates training/serving skew, accelerates experimentation, and ensures feature consistency across teams and models.
Statistically rigorous experimentation platforms for model comparison, canary deployments, and gradual rollouts with automated significance testing.
A disciplined ML lifecycle that reduces time-to-production while maintaining scientific rigour.
Define the business problem as an ML problem. Establish success metrics, baseline performance, and feasibility assessment.
Data collection, cleaning, feature engineering, and labelling pipelines. Ensure data quality and representativeness for training.
Iterative experimentation with tracked experiments, hyperparameter tuning, and model selection based on business-relevant metrics.
Containerised model serving with CI/CD, automated testing, shadow deployments, and gradual traffic shifting.
Ongoing monitoring, drift detection, automated retraining, and performance reviews to maintain and improve model quality.
We work with the best tools in the ML ecosystem, selecting the right platform for each use case.
ML systems we have built and deployed for enterprise clients.
Demand forecasting, customer lifetime value prediction, and risk scoring models that integrate directly into business workflows and decision systems.
Forecasting · ScoringPersonalisation engines using collaborative filtering, content-based, and hybrid approaches for product recommendations, content curation, and search ranking.
Personalisation · RankingReal-time anomaly detection for fraud prevention, cybersecurity, and operational monitoring, using statistical and deep learning approaches.
Fraud · Security · OpsLarge-scale text classification, summarisation, and entity extraction pipelines processing millions of documents with transformer-based architectures.
NLP · TransformersHeadquartered in Islington, London. We work on-site, hybrid, or fully remote to suit your team's needs.
Trusted by Siemens, Imperial College London, UCL, and other industry leaders across multiple sectors.
From data preparation through model development to production serving and monitoring -- we own the full ML lifecycle.
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From problem framing to production monitoring -- we partner with enterprises to build ML systems that deliver measurable ROI.
Schedule a Consultation