A selected record of AI applications and machine learning systems built, validated and deployed for UK and international clients. Every record includes input, method, inspection status and measurable outcome.
Five selected delivery records representing the scope and quality standard of our AI engineering work. All systems below are currently operational in client production environments.
A UK multi-site retailer required a systematic approach to demand planning to reduce inventory waste and stockout incidents across a large and diverse product range. Manual forecasting was producing inconsistent results and the buying team lacked confidence in the numbers underpinning purchasing decisions.
We designed and built an end-to-end demand forecasting platform that ingests point-of-sale data, inventory levels, seasonal signals and external demand indicators to produce 12-week forward forecasts at SKU and store level. The system includes a confidence interval layer, an exception dashboard for the buying team and an automated reorder trigger API.
A UK-based fintech payments platform was experiencing a rising fraud rate that manual review processes could not keep pace with at transaction volume. The client required an automated detection system that could operate at millisecond decision latency without creating unacceptable false-positive rates that would degrade legitimate customer experience.
We built a real-time anomaly detection system using an ensemble of gradient boosting and neural network classifiers trained on transaction history, behavioural signals and contextual features. The system operates at under 80ms decision latency with configurable risk threshold controls and integrates directly with the existing transaction processing infrastructure via API.
A UK legal services firm was processing high volumes of contracts, settlement agreements and correspondence through manual review, resulting in slow turnaround times and extraction inconsistency. The client required an automated system capable of identifying and extracting specific clauses, obligations, dates, parties and monetary values with a configurable accuracy threshold.
We built an NLP-based document intelligence pipeline using a fine-tuned transformer model for legal domain entity extraction. The system processes documents via a REST API, returns structured JSON output with confidence scores for each extracted entity and routes documents with low-confidence extractions to a human review queue with the relevant fields flagged.
A UK precision manufacturing client was conducting 100% visual inspection of components manually, creating a bottleneck at a critical stage of the production line and introducing human variability in defect classification. The client required an automated inspection system with a detection rate matching or exceeding their experienced inspectors and a full audit trail for quality certification.
We designed and deployed a computer vision system using a convolutional neural network trained on labelled inspection images covering all known defect categories. The system operates in-line, triggering a pass or fail classification with defect type and location annotation per component, at the required inspection throughput rate without impacting line speed.
A UK telecommunications provider was experiencing above-average customer churn with no systematic capability to identify at-risk customers early enough to deploy retention interventions. Account management teams were operating reactively on cancellation notices rather than proactively on churn risk signals.
We built a churn prediction model trained on account history, usage patterns, billing events, support interaction data and service quality signals. The model produces a weekly churn risk score per active account, ranked by urgency, integrated directly into the client's CRM system as a dashboard layer, with recommended intervention action and priority classification.
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