Flagship projects across sectors where AI had to move from idea to deployed capability. Each example reflects work I led or built directly.
Healthcare · Hospital · Regulated AI
AI-driven clinical data integration and visualisation for specialty-specific insights
Challenge: A major hospital needed to unlock clinical insight trapped in unstructured reports and disconnected systems, and bring it into clinicians' day-to-day workflows.
What I led: A strategic AI initiative aligning stakeholders from C-level executives to data scientists and engineers. Brought together NLP pipelines for extracting structured data from PDF reports, FHIR-based interoperability, and clinician-facing dashboards into one specialty-specific solution.
Outcome: A solution that addressed real clinical needs, enabled specialty-specific insights, and contributed to published work.
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Healthcare · NLP · Interoperability
Genomics NLP pipeline to FHIR and clinician-facing tooling
Challenge: Genomic variant information was locked inside unstructured PDF reports, slowing clinical use.
What I built: Architected an end-to-end pipeline extracting genomic variants from unstructured PDFs, mapping outputs to FHIR, and feeding a clinician-facing dashboard. Part of a broader hospital data transformation initiative.
Outcome: Significantly reduced manual review effort and created a reliable route from report intake to clinical decision support.
Enterprise AI · LLMs · Governance
Enterprise RAG / LLM delivery in a regulated setting
Challenge: Internal teams needed trustworthy answers to compliance and policy questions within their existing workflows, with strict governance expectations.
What I led: Architected and shipped an enterprise compliance-focused assistant with governance, traceability, and source attribution built in from the start. Led product and delivery direction through to rollout to approximately 1,500 internal users.
Outcome: Hands-on delivery of a modern AI system where control, reliability, and adoption mattered as much as model capability.
Telecommunications · Commercial ML
Telecom uplift modelling for call reduction
Challenge: A telecommunications client needed to reduce inbound call-centre demand driven by bill shock, where customers receive a higher-than-expected bill.
What I built: Developed an uplift modelling solution combining propensity modelling with treatment-effect estimation to identify customers most likely to call and recommend the most effective proactive interventions, such as billing alerts and personalised messages.
Outcome: Validated through A/B testing and used to support more targeted, timely customer engagement.
Accenture case study
Financial Services · Predictive ML
Delinquent invoice prediction
Challenge: A financial-services client needed a more reliable way to flag invoices at risk of non-payment so collections effort could be prioritised.
What I built: Applied machine learning techniques to the existing prediction approach and integrated the result into a client-facing decision workflow.
Outcome: Improved delinquent invoice prediction performance by 10% over the earlier model, supporting more reliable identification and prioritisation of at-risk invoices.
Property Analytics · Deployed ML Product
Cyprus Automated Valuation Model (AVM)
Challenge: The Cypriot property market suffers from low transaction volume and variable data quality, making automated valuation hard.
What I built: Developed and deployed an ensemble-based AVM tailored to the local market in collaboration with an experienced property valuator, incorporating external data sources including satellite imagery.
Outcome: A highly accurate production valuation model running on Google Cloud Platform.
Insurance · NLP
Automated clause detection in policy documents
Challenge: Reviewing insurance policy documents for specific clauses was slow, repetitive, and inconsistent.
What I led: A team of two data scientists developing NLP models for clause detection using Hugging Face models and modern NLP practices.
Outcome: A proof of concept that clearly demonstrated how AI could automate parts of the document review process and improve efficiency and consistency.
