Retail decisions happen daily and add up over millions of SKUs and customers. We sharpen the data and the processes underneath those decisions so AI shows up as judgment, not noise.
Retail teams drown in dashboards. The pain is not "no data", it is too many slightly different versions of the same metric across teams. Consolidation beats new analytics every time.
Personalization models are easy to build and hard to keep fresh. The bottleneck is the data refresh cadence, not the algorithm.
Demand and supply teams optimize against each other when the underlying data does not match. A shared data definition is the cheapest forecasting improvement available.
Process re-engineering is where retail AI actually compounds. Models that bolt onto a broken process inherit the brokenness.
Customer names withheld. Patterns are real.
A retail client had grown by acquisition and inherited five overlapping ways of doing the same operational tasks: replenishment, returns, store ops, vendor onboarding, promotions. We mapped the actual workflows, simplified the ones that had drifted, and deployed governed automations on top. Cycle times dropped on the workflows we touched, and the team kept the playbook for the rest.
Consolidated dozens of conflicting dashboards into a single decision surface backed by a governed data layer. Merch, marketing, and supply chain stopped disagreeing about definitions and started disagreeing about strategy, which is the disagreement that actually moves the business.
Built a data foundation that combined point-of-sale, e-commerce, and supplier signals with lineage, then layered ML-assisted demand forecasting on top. The forecasting team could explain every adjustment to the planning team, which is what made the rollout stick.
Bring us the messy version. We will tell you whether the data foundation, the process, or the model is the real bottleneck, and what we would build first.
