Full case studies with outcome-first summaries, stack, and proof. Use filters to narrow by type of work, or open the impact record for a recruiter-friendly overview.
Showing 8 of 14 matching projects.
Centralised analytics hub with ~100 monthly active users
Analyticado made trusted analytics easier to find. Dashboards, metadata, and usage signals sit in one searchable hub, supporting roughly 100 monthly active users and roughly a tenfold lift in engagement compared with the previous scattered entry points.
Internal / Enablement
Analyticado became the organisation's main place to find analytics: Looker dashboards and other outputs in one searchable site. Custom search, connections to Looker's APIs, embedded charts, and screens built for clarity meant analytics use rose about tenfold with roughly 100 active users each month, the default place to discover dashboards and reports. Plain language and layout supported adoption. Pipelines load Looker metadata, curated spreadsheets, and usage events; database design and internal privacy rules governed what metadata could show.
One chat for sourced answers and conversational analytics
K-AI gives teams one place for sourced answers from internal docs and the dashboard catalogue, plus conversational analytics when the question is really about numbers. Routing is tuned so people land on trustworthy explores, long answers run reliably behind corporate proxies, and analytics owners could tighten behaviour before wider rollout.
Internal / Enablement
Delivered a production chat assistant on the company analytics website. For general questions it searches internal documentation and dashboard catalogue material and answers with citations. When the question is really about numbers or metrics, it hands off into Looker's conversational analytics in the right context. Combines keyword and meaning-based search, AI-powered routing when a question could belong to more than one business area, and careful matching so users do not land on the wrong dashboard. Long answers run as background jobs with polling so sessions stay reliable behind corporate proxies. Uses standard cloud APIs for orchestration and optional storage for job history. Rolled out in stages with configuration so behaviour could be tightened over time.
Cross-platform photo sharing app on iOS & Android
Independently shipped Capture on iOS and Android: shared photos surface after a delay, with subscriptions, one-time purchases, and ads configured end to end. Public metrics on this site show acquisition and retention moving as the product ships.
Personal / Product
Independently designed, built, and launched a cross-platform phone app (Flutter) for delayed-reveal photo sharing. Owned the idea, branding, build, how to charge for it, and how the app stores present it. Backend on Supabase (database, sign-in, file storage, access rules); push notifications, in-app purchases, and ads wired in. UI designed in Figma. Launched January 2026 with roughly 100 downloads and ongoing growth.
Used data to decide when delivery routes could open
Checked real booking behaviour against assumptions and showed where demand was missed. Explained delivery-slot and area-setup ideas clearly to leaders. Built charts that highlighted patterns and delivery areas that were too large or poorly sized.
Last Mile
Checked real customer booking behaviour against what planners assumed, and showed demand was not captured as well as people thought. Gave leadership confidence to automate when delivery routes open and close. Explained delivery-slot timing, area setup, and routing ideas in plain language to executives. Turned very large datasets into a short list of insights. Used diagnostic analysis to find why slots failed. Built efficient SQL over operational tables and charts that highlighted patterns, odd spikes, and delivery areas that were too large or poorly drawn. Automated refresh with spreadsheet scripts. Kept privacy and data accuracy in scope.
Whether big sales on heavy items actually made money after real costs
Compared baseline and promotion weeks for high-volume bottled water and multipack soda across partner FCs: pulled together orders, delivery cost per tote (driver, fuel, totes per route), tote fill from bulky volume, opportunity cost versus average basket profit, and incremental route pressure. Estimated on the order of $420K profit impact at the largest site over a seven-day window; ~$560K combined across two FCs versus baseline; rounded network-scale exposure on the order of ~$1M once fully costed. Built partner-facing slides for commercial choices (guardrails, thresholds), not headline sales only. Presented the deck directly to the client: methodology walkthrough, Q&A, and alignment between commercial and fulfilment stakeholders.
Retail fulfilment / Commercial
Built a full-cost view of large promotions on heavy, space-hungry categories for online grocery during a secondment to the US. Compared baseline weeks to promotion weeks across partner fulfilment sites for bottled water and multipack soda so higher demand could be read together with delivery cost per shopping tote (driver wage and hours per route, fuel split across totes per order), tote fill driven by bulky cube, warehouse handling, and opportunity cost when bulky lines displaced profit from a typical basket mix, not headline sales alone. In one representative seven-day window, the executive view pointed to roughly $420K estimated profit impact at the largest analysed FC; combining two FCs in the model yielded roughly $560K estimated downside versus baseline once both categories were fully costed. The deck also framed rounded network-scale exposure on the order of ~$1M once fully costed. Split costs across unloading, picking, packing, loading, and delivery; documented assumptions and overlapping promos where relevant. Delivered partner-facing slides with recommendations: contribution thresholds after fulfilment and opportunity cost, tighter mechanics or caps for high-space low-margin SKUs, and FC-specific review where economics differ. Presented the deck directly to the client: walked through methodology, handled Q&A, and aligned commercial and fulfilment stakeholders on next steps. Outcome: leadership could approve promos on profit impact, not lift alone.
~$1M annual savings through operational optimisation
Model that spotted where fresh groceries cost too much to pick and deliver for a large grocery partner. Clear recommendations, major annual savings, and a version the client's team could run themselves. Presented the savings case and model logic to partner stakeholders to drive adoption.
CFC Operations
Developed the Cost to Serve model that found where fresh groceries cost too much to pick and deliver for a major grocery partner. Recommendations led to about $1M a year in savings. Built a version the client's team could run themselves; operations leadership adopted it. Presented the savings case and model mechanics to partner stakeholders so adoption was grounded in trust and clarity. Used SQL and structured analysis to trace costs to real drivers and explain causes in plain terms. The model became the default reference when discussing fulfilment cost.
Maps showed delivery-area and planning problems
Interactive maps for booking patterns and problem areas. Filters for time period, site, and slot-planning region. Surfaced issues such as oversized delivery zones.
Last Mile
Spotted that leadership could not see booking problems clearly on a map and built interactive maps for availability patterns and weak delivery areas. Showed oversized zones and fed into better route timing and slot planning. Filters for time range, site, and planning region. Surfaced issues such as slots disappearing at peak times. Grew from early notebook experiments to multi-partner tools. Structured data so locations and zones compared fairly.
Clearer view of orders and staffing needs
Live breakdown of orders and how much labour they imply. Shaped views around what operations asked for. Same-day monitoring for day-to-day decisions.
CFC Operations
Built live views of how orders break down and how much labour they imply, for partners and internal teams. Helped tighten day-to-day staffing with same-day monitoring. Matched the logic to how operations actually decide things. Traced what drove live order volumes and labour needs. Efficient SQL for live and historical figures with quality checks. Charts that stay easy to read under pressure. Automated refresh via spreadsheet scripts.