01 — Context
Modern retail is a data problem. Customers interact across stores, websites, apps, and call centres — leaving behind signals that, connected and analysed, reveal exactly what they want and when. Inventory sits in warehouses while shelves are empty because demand signals are not reaching procurement. We help retailers connect the dots.
02 — The Challenge
The Unified Retail Data Challenge
Online and offline data live in different systems with different identifiers, different schemas, and different update frequencies. A customer who bought in-store last Tuesday and browsed online yesterday looks like two different people. Inventory data in the warehouse management system does not match the ERP, which does not match the website. Demand forecasts are built on historical sales without incorporating real-time signals like search trends, social sentiment, or weather.
03 — Our Approach
We build the Customer Data Platform that resolves identities across channels and creates a unified customer view. Real-time inventory pipelines give a single, accurate view of stock positions. ML forecasting models incorporate multiple signal types — historical sales, seasonality, promotions, external factors — to produce SKU-level demand predictions. Personalisation engines use customer behaviour graphs to drive recommendations, targeted offers, and campaign segmentation.
Engineering Approach
Built from omnichannel data unification pipelines, Customer Data Platforms, real-time inventory event streams, and time-series ML forecasting infrastructure.
04 — Business Impact
What Changes
Unified customer identity across online and offline channels
SKU-level demand forecasting reducing stockouts and overstock
Personalised experiences that increase conversion and retention
05 — Domain Capabilities
What We Build
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Let us show you what is possible with your data and AI.