Featured Case Studies
These projects show how I turn disconnected data, manual processes, and unreliable reporting into production systems that support revenue and better decisions.
Where a project came from client work, the client is anonymised and the figures are rounded from systems I shipped and maintained. The invoice review is a product I built for small businesses that still check supplier invoices by hand.
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Automated Repeat Revenue Engine
£5M+ in attributed repeat revenue supported during active co-ownership.
Strengthened an automated customer-reminder journey with self-healing product coverage, stable events, and end-to-end monitoring.
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Product Launch Profitability System
14k+ orders made measurable with 99.97% cost coverage.
Connected supplier, bundle, packaging, shipping, and order data so leaders could see the true economics of a new product line.
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£50M+ in advertising spend matched to product performance.
Replaced broad proportional allocation with campaign-level product attribution and consistent multi-currency reporting.
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5,000+ weekly operational actions made visible.
Rebuilt time-aware reporting so managers could see real workload, throughput, completed work, and overdue activity.
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Five demographic views made reusable and self-service.
Turned difficult survey exports into governed analysis for single-select, multi-select, and demographic comparisons.
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5,000+ inbound contacts reconnected to the orders they concerned.
Repaired the matching path between customer contacts and orders so service demand could be measured more accurately.
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Approval blocked until VAT, totals, and duplicates pass.
A review a small business can run on every supplier invoice, with errors sent back to the supplier for a document the same rules check again.
What these projects demonstrate
- End-to-end ownership from business problem to production delivery
- Clear separation between measured impact and personal contribution
- Data systems designed for reliability, auditability, and ongoing maintenance
- Architecture and reporting that non-technical teams can use to make decisions
- AI workflows where the model reads the document and deterministic rules decide what can be approved
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