Retail
Demand, personalization and supply-chain intelligence
Retail decisions are made against data that changes hourly. We build platforms where demand signals, inventory position and customer behaviour are available while they still matter.
Industry challenges
The conditions we design around
Perishable decisions
Pricing, replenishment and allocation decisions lose value within hours of the signal that should inform them.
Channel fragmentation
Store, e-commerce and marketplace data arrive in different shapes with different definitions of a sale.
Seasonal load
Peak trading multiplies both traffic and analytical demand against infrastructure sized for the average.
Inventory truth
Available-to-promise depends on positions that are stale by the time they reach the decision.
Data challenges
Where the data work actually is
These are the problems that determine whether analytics and AI initiatives in this sector succeed.
- Product and customer identity resolution across channels
- Consistent definitions of sale, return and margin
- Streaming inventory movements alongside batch financial data
- Long history retained affordably for seasonal modelling
- Consent-aware handling of customer behavioural data
AI opportunities
Where AI is worth the investment here
Demand forecasting
Location and SKU-level forecasts feeding replenishment and allocation decisions.
Personalization
Recommendation and ranking models grounded in current catalogue and inventory availability.
Supply-chain exception detection
Models surfacing anomalies in flow, lead time and fulfilment before they reach the customer.
Cloud transformation
What moving to Google Cloud unlocks
Streaming inventory
Event-driven movement data so available-to-promise reflects reality rather than last night.
Elastic peak capacity
Serverless analytics that absorb peak trading without provisioning for it year-round.
Unified commerce model
One semantic model across channels so margin means the same thing everywhere.
Security & compliance
Considerations we design for
We architect towards your obligations and document the controls we implement. We do not claim regulatory certifications on your behalf or ours.
Customer data handling
Consent state captured with behavioural data and enforced in downstream modelling.
Payment data boundaries
Architecture that keeps payment data out of analytical stores by design.
Retention discipline
Category-specific retention applied automatically rather than by periodic cleanup.
Architecture patterns
Patterns we apply in this sector
- Streaming + batch
- Pub/Sub movement events alongside batch financial loads into one BigQuery model.
- Feature store for demand
- Shared features serving both forecasting and personalization models.
- Availability-aware ranking
- Recommendations filtered by live inventory before they reach the customer.
- Tiered history
- Storage tiering that keeps multi-year seasonal history affordable.
Example use cases
Engagements typically look like this
Illustrative scopes based on the patterns above. No client names or results are implied.
- SKU and location-level demand forecasting
- Real-time available-to-promise across channels
- Personalized ranking grounded in live availability
- Markdown and pricing analytics
- Supply-chain exception monitoring
Start a conversation
Discuss a retail engagement.
Bring the constraint you are working within — regulatory, operational or technical — and we will tell you what a workable architecture looks like.