Problems solved.
Outcomes measured.
We take on fewer engagements so we can do each one completely. These are a selection of the problems we've been trusted to solve — told the way we think about them: challenge, approach, outcome.
Rebuilding credit risk decisioning from the ground up.
A regional lender was running a decade-old scorecard that hadn't been recalibrated since the rate environment turned. Default rates were creeping up, the manual override process had become a compliance liability, and nobody could explain why a given applicant was declined.
We audited the existing model, rebuilt the feature-engineering pipeline, and deployed an ensemble trained on five years of repayment data. Crucially, we framed the objective around approved volume at a fixed loss tolerance — not raw accuracy — and built a profit curve the credit committee could read.
Defaults fell 18% while approval volumes held steady. Automated retraining triggers keep the model current, and a regulator-ready model card now satisfies both internal audit and external supervision on the first pass.
Demand forecasting across 800+ SKUs in a volatile supply chain.
Post-pandemic demand patterns had made a spreadsheet-based forecasting process unmanageable. The merchandising team spent more time on manual adjustments than on decisions, and overstock write-offs were eating into margin.
We designed a hierarchical forecasting system — bottom-up at SKU level, reconciled top-down to category targets — with external signals (weather, holidays, promotional lift) built in. The whole pipeline runs automatically and feeds the ERP replenishment system directly.
Overstock write-offs dropped 22%, manual planning time halved, and the freed-up margin funded the next phase of work. Forecasts now carry honest confidence intervals, so buyers know which numbers to trust.
Reducing no-shows through appointment-level propensity scoring.
A 14-location clinic network was running a 19% appointment no-show rate — a direct hit to revenue and a scheduling headache that rippled across every site. Blanket reminders weren’t moving the number.
We built a real-time propensity model that scores each appointment at booking and again at the 48-hour reminder. High-risk appointments trigger a differentiated outreach workflow. The model sits inside the existing patient-management system, with no change to clinical staff routines.
No-shows fell by nearly a third, recovering an estimated £1.4M in annual revenue — without adding a single step to clinicians' day. Schedulers now see risk at a glance and intervene only where it counts.
A subscriber lifetime-value model to guide acquisition spend.
A digital publisher was spending aggressively on subscriber acquisition with no clear view of which channels delivered high-value readers versus short-tenure churn. Every channel looked fine on day-one conversion; the truth only showed up months later.
We built an LTV model that separates predicted engagement from tenure, segments acquired subscribers into behavioural cohorts, and surfaces payback period by channel in a live dashboard for the marketing team. Cohort retention became the lens for every spend decision.
The LTV:CAC ratio improved 34%, and the marketing team reallocated budget within 60 days of launch. The strongest cohorts now retain 11 points above the blended average — and the team can see it the moment it happens.
Consolidating eight data sources into a single operational picture.
The operations team worked across eight disconnected systems — route planning, driver apps, customer portals, fleet telematics, and more — none of which agreed with each other. Dispatchers were reconciling spreadsheets instead of running the network.
We designed a unified data layer, built the transformation models in dbt, and delivered a single operational dashboard giving dispatchers a real-time view across every asset and active delivery. The warehouse now serves as the backbone for all downstream analytics.
Eight systems became one source of truth, SLA performance improved 11%, and the operations team finally spends its time on decisions rather than data wrangling. Every later analytics project now starts from this foundation.