Databricks for Retail & CPG
Infocepts helps retailers and consumer brands unify customer, product, store and supply-chain data on Databricks to improve growth, profitability, customer experience and operations.
Accolades
Databricks Brickbuilder Silver PartnerDatabricks Partner Network
Ranked #1 by DatabricksElevate & Accelerate – The Rising Challenge
Databricks Brickbuilder Security & Governance SpecializationDatabricks Brickbuilder Network
Databricks Brickbuilder Retail, Consumer Goods & Travel SpecializationDatabricks Brickbuilder Network
Proof in Production
Each one has a defined data requirement and a measurable target agreed before build starts.
Demand
Hierarchical forecasting at SKU-store grain with promotion effects, seasonality, and separate handling for intermittent demand.
Measured on
Forecast accuracy against your current baseline, by assortment segment.
Operations
Exception detection across labor, availability, and equipment, routed to store managers in the system they already use.
Measured on
Exception resolution time and on-shelf availability.
Customer
Identity resolution across channels, propensity models, and real-time decisioning connected to your martech stack.
Measured on
Conversion and engagement lift against a holdout group.
Margin
Elasticity modeling, promotion incrementality, and markdown optimization within your commercial constraints.
Measured on
Gross margin and sell-through at end of season.
Supply
Multi-echelon inventory modeling and allocation logic that reflects real lead times and distribution constraints.
Measured on
Stockout rate, inventory turns, and carrying cost.
Foundation
Governed definitions of sell-through, open-to-buy, margin, and incrementality, consumed by BI and AI alike.
Measured on
Reduction in conflicting reported numbers across teams.
Why this matters
Industry context turns a platform capability into a solution people can actually adopt. Infocepts combines domain expertise with Databricks engineering and governance.
Connect POS, ERP, inventory, workforce, ecommerce, loyalty and operational data into a governed intelligence layer.
Use computer vision and AI to identify stockouts, misplaced products, pricing mismatches and planogram deviations.
Combine assortment, pricing, demand and customer signals to improve planning and faster merchandising decisions.
Build ML-powered demand forecasts with proactive alerts for stock risk, supplier delays and promotional uplift.
Solutions
Retail
AI-powered store operations, computer vision, shelf monitoring and on-shelf availability.
Explore OptiStoreAI
Retail
Assortment, pricing, supply-chain and store execution intelligence on Databricks.
Explore connected intelligence
Retail
ML-driven demand forecasting, alerts and explainable risk signals.
Explore supply chain forecasting
Retail
Global planning and supply/execution data unified through governed data products.
Explore the data platformBusiness impact
11
production-ready retail and media AI solutions showcased at DAIS 2026
40%
faster average time-to-value across Infocepts Databricks programs
300+
Databricks certifications earned in a single quarter during the Rising Challenge
100%
Explainable forecasts, every driver visible
Build the next Databricks use case.
Tell us the business outcome and the current data environment. We’ll help shape the path to production.
Insights & Resources
Guide
Questions we get asked
For demand forecasting, at minimum two to three years of transaction history at SKU-store-day grain, a product hierarchy, and a promotional calendar. Store operations work needs current operational telemetry rather than deep history.
Data quality usually matters more than volume. A shorter clean history beats a longer inconsistent one, and promotional data recorded properly is worth more than an extra two years of transactions.
Slow movers need different treatment from high-velocity items. We segment the assortment by demand pattern and apply appropriate methods to each, then report accuracy by segment. A single portfolio-level accuracy number usually hides poor performance on the long tail, which is where the inventory cost sits.
Usually it complements rather than replaces. Databricks generates the forecast, the elasticity model, or the allocation recommendation; the planning system stays the place where planners work. Integration design matters more than replacement, and planner adoption depends on it.
We agree the baseline before build starts — usually your current forecast, measured the same way — and hold out a period for evaluation. Accuracy gets reported by assortment segment and by forecast horizon, because improvement is rarely uniform across either.
Yes. The data model differs — CPG works from shipment and syndicated data rather than point of sale — but the forecasting, promotion measurement, and supply chain work carry across. Retail media measurement is an increasingly common request from CPG clients.
