Industry · Retail & E-Commerce

Data Engineering for Retail

Real-time inventory, Customer 360, and demand forecasting — event streaming and lakehouse platforms proven in production with 50M daily events at 500ms latency.

Problems We Solve

Industry Challenges

Oversells destroy trust

When inventory lags reality, you sell what you don't have. Our AWS Kinesis + Lambda streaming platform processes 50M daily inventory events with 500ms update latency — oversells eliminated.

Customers are fragmented across channels

Web, app, store, and loyalty data describe the same person four different ways. We build Customer 360 platforms — our Databricks engagement unified 8M customers and drove an 18% revenue lift through ML personalisation.

Forecasts run on stale data

Demand planning fed by week-old batch extracts misses the moment. Our GCP supply chain lakehouse unified 15 regional logistics systems and improved forecast accuracy by 35%.

Peak season is a stress test

Black Friday traffic multiplies event volume overnight. We design serverless, auto-scaling streaming architectures that absorb peaks without re-platforming.

Proven In Production

Measured Results

50M
Daily inventory events
500ms update latency
18%
Revenue lift
ml personalisation · 8m customers
35%
Forecast accuracy gain
15 logistics systems unified
Evidence

Related Case Studies

Questions, Answered

Retail FAQ

How fast can inventory updates propagate?
Sub-second — our production AWS Kinesis + Lambda platform holds 500ms end-to-end update latency at 50M events per day, keeping storefront availability honest across channels.
Can you unify online and in-store customer data?
Yes — identity resolution across web, app, POS, and loyalty into a governed Customer 360. Our retail engagement unified 8M customer profiles on Databricks and fed ML personalisation that lifted revenue 18%.
Do you integrate with our existing commerce stack?
Yes — Shopify/commercetools-style platforms, ERPs (SAP, NetSuite), OMS/WMS systems, and clickstream tools, connected through event streams or CDC rather than brittle nightly exports.
How do you handle seasonal traffic spikes?
We design for the peak, not the average: serverless streaming (Kinesis/Lambda or Pub/Sub/Dataflow), autoscaling consumers, and idempotent processing so Black Friday is just another day.
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