Machine learning that predicts, optimises, and transforms how you operate.
Typical demand forecasting model accuracy
Average reduction in customer churn achieved
Average ROI within first quarter of deployment
Demand forecasting, churn prediction, risk scoring, and lead scoring models trained on your specific business data and continuously improved over time.
AI-powered customer clustering that uncovers behavioural segments and lifetime value cohorts your team would never find manually, and keeps them updated automatically.
Document classification, sentiment analysis, intelligent text extraction, and conversational AI built on the latest LLMs and tailored to your business context.
Production-grade ML pipelines with automated monitoring, drift detection, retraining triggers, and model performance dashboards, keeping models accurate over time.
Built a customer health scoring model for a SaaS platform that flagged at-risk accounts 45 days before cancellation, enabling the CS team to intervene, reducing monthly churn rate by 28%.
Deployed a demand forecasting model across 3,000 SKUs for a retailer, achieving 92% accuracy vs a previous manual process at 71%, reducing overstock by £1.4M annually.
Built an NLP pipeline that extracts and classifies data from 10,000+ financial documents per month, replacing a manual review process that took 3 FTEs 2 weeks each cycle.
We bring deep domain knowledge across these sectors
Define the business question, success metrics, data requirements, and ROI target.
Collect, clean, and engineer the features the model will learn from.
Train, evaluate, and iterate on models until business accuracy metrics are met.
Deploy to production with monitoring, alerting, drift detection, and retraining pipelines.
Not always. Many high-value ML use cases work with moderate data volumes, 12–24 months of historical data is often sufficient. We assess data readiness in discovery and recommend the right approach for your situation.
A focused predictive model, churn, demand, or risk, typically takes 6–10 weeks from data assessment to production deployment. More complex models or multi-output systems take longer.
We deploy full MLOps monitoring, accuracy metrics, drift detection, and business outcome tracking. You can see model performance in real time and receive alerts when retraining is needed.
No. AI augments analysts by handling repetitive pattern recognition at scale, freeing your team to focus on interpretation, strategy, and decisions that require human judgment.
Yes. Model outputs can be fed directly into your CRM, ERP, BI dashboards, or operational systems via API, so predictions flow automatically into the decisions they are designed to inform.
We apply explainability techniques (SHAP, LIME) so every prediction can be understood and audited. For regulated use cases we build in bias testing and document model cards for compliance purposes.
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