Every case study here is a real business that had a real data problem. We don't anonymise away the hard parts. Here's what we built and what it changed.
A multi-platform global retailer had accumulated customer records, purchase history, and supply chain events across 14 separate systems built over a quarter-century. Nobody had a complete picture. Every department was working from a different version of reality. Finance said one revenue number. Operations said another. The supply chain had no visibility beyond the current week.
We designed the architecture, built the transformation layer, trained the predictive models, and deployed department-specific BI views — all live, while the business kept running at full pace. Zero downtime. No data loss. One platform.
A financial services platform was catching fraud after the fact. We rebuilt the detection architecture on Kafka Streams with ML inference in real-time — decisions under 200ms. The previous batch took 6 hours and was already stale when it ran.
Investors were stress-testing the technical scalability story and the founding team didn't have the language for it. We came in as fractional CTO, audited the stack, identified the three real risks, and built a roadmap that made due diligence clean.
A logistics company was paying $400K/year in Oracle licensing for a warehouse they'd outgrown. We moved the entire system to GCP, rewrote the reporting layer in Looker, and cut their annual data infrastructure spend by 60% — with no downtime.
A mid-market fashion retailer was over-ordering based on gut instinct and last year's numbers. We built a multi-variable demand forecasting system incorporating weather patterns, social signals, and regional sell-through data — deployed live within 8 weeks.
A lending platform's customer success team was toggling between four systems to answer a single support call. We built a unified Power BI view pulling from Salesforce, Stripe, and Zendesk — every customer's full context in one screen.
An analytics SaaS company had four analysts manually checking quality across 300+ client pipelines every morning. We deployed an agentic AI system that monitors, flags, and auto-resolves 92% of issues — with a human escalation path for edge cases.
A third-party logistics provider had no single view of inventory across 50+ warehouse locations. Shipment ETAs were manual estimates. We built a unified real-time platform that aggregated IoT sensor data, WMS feeds, and carrier APIs into a single operational dashboard.
A media subscription company was losing subscribers with no warning signal. We built a churn prediction model trained on engagement patterns, payment behaviour, and content consumption — feeding a daily risk score into the retention team's CRM workflow.
A lending fintech had 6 weeks before a regulatory audit with no documented data lineage, no data dictionary, and no access control audit trail. We implemented a full governance layer on Collibra, mapped 400+ data assets, and they passed with no findings.
Every engagement starts with one conversation. Tell us about your data challenge — we'll tell you honestly what's possible.
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