Selected Work // Verified Outcomes
Six use-cases. Different problems, different functions, different scales. The pattern is consistent: independent expertise identified what the problem actually was — and what it was worth to solve correctly.
All outcomes are scaled to the nature of the business, the problem, and the use case. For your organisation, value will be quantified on a case-by-case basis.
A legal team held thousands of contracts with no systematic way to identify under-enforced or high-value terms.
Ran a proof of concept against a single contract term across 1,000 contracts. Surfaced $1.3M in enforcement value. Expanded to four terms. The business case for a seven-figure enterprise platform followed.
Adopted across three divisions, 200+ users. The entire platform investment was justified by one proof of concept on one term.
The platform was not the insight. The proof of concept was.
A fragmented data environment was blocking AI delivery at scale. No shared infrastructure existed for cross-functional AI deployment.
Directed and oversaw onboarding of a Data and AI Ops platform — a foundational infrastructure requirement enabling both the legal analytics use case and a parallel supplier optimisation initiative.
A $3M platform agreement signed. A single use case — automation of electronic parts across the manufacturing supply chain — is forecast to deliver a 10x ROI on that agreement.
Quality teams were manually processing over 100,000 raw quality records — an effort requiring tens of thousands of labour hours to produce meaningful analytics.
Developed and deployed an ML solution that analysed and clustered the full dataset, delivering a real-time analytics dashboard that replaced manual processing entirely.
A process requiring entire quality teams and tens of thousands of hours now runs continuously and in real time.
Deviation investigation processes across a manufacturing network were slow and resource-intensive. Industry pricing for minor deviations ranges from $2,000 to $5,000 per event.
Developed an agentic platform that automates and accelerates the deviation investigation workflow end-to-end. Currently in prototype deployment across the manufacturing network.
Forecasted up to 30% reduction in investigation cycle time. Projected $1–3M in deviation cost reduction per manufacturing site.
Across three functions — engineering, quoting, and quality — time spent locating information was a consistent productivity drag. A process that should take seconds was taking minutes.
Deployed targeted knowledge base solutions across all three functions, each scoped independently to fit the specific information environment of that team.
Time-to-information reduced by an order of magnitude across all three deployments. Ten minutes to ten seconds.
An external-facing client platform required AI-powered summarisation. An external supplier build was being considered, but a longer-term internal roadmap made vendor spend inefficient.
Tasked the internal team with rapid development using generative AI development practices to compress the build cycle.
Feature delivered without vendor spend. Demonstrated in practice what is commonly marketed as a 5x–10x engineering productivity multiplier.
Disclaimer // All use-cases anonymised.