RETAIL & E-COMMERCE

Retail & e-commerce data migrations.

Move from overnight updates to near-real-time, so your merchandising, marketing, and operations teams can act on what's happening today instead of yesterday. We pull your online store, ERP, store registers, and ad platforms into one trustworthy source.

What you get

Built for the retail & e-commerce reality.

Same-day view of inventory, sales, and ad spend together
One clean view of each customer across web, store, and call center
Near-real-time data for promotion and pricing decisions
A tested model for which marketing drives sales, with its assumptions written down

1 hour

From a sale to it showing in your data (down from 18 hours)

+18%

Faster promotion decisions in the first quarter after the move

31%

Lower running costs after right-sizing during peak

What we hear from buyers

Three problems we solve over and over.

Three copies of the same data

The usual pattern: one for product, one for marketing, one for finance. We bring them together into a single, checked stream so every team works off the same numbers.

Promotion math the merchants actually trust

If the dashboard says a promo lifted units 12% and the buyer's gut says 4%, the dashboard loses. We build numbers the people closest to the business can confirm for themselves.

Costs that hold up during a busy season

Black Friday isn't the time to discover your setup can't keep up. We plan for real demand and test against 5x your normal load before peak.

AI Enablement deep-dives

Where this stack meets AI.

The deep-dive pages below cover the AI builds we see most often on top of a healthy retail & e-commerce data foundation.

ML productionization

Turn pricing, likelihood-to-buy, recommendation, and demand-forecast models from one-off experiments into a system your team can run every day — with a shared library of the data they rely on and tests that check they're still accurate.

Read the deep-dive

Databricks Mosaic AI

Build AI tools right where your sales, ad, and inventory data already lives on Databricks — including the part that lets the AI search your content by meaning — so there's no second system to secure.

Read the deep-dive

RAG implementation

Self-service product questions, customer help assistants, and merchant-facing assistants — all answering from your own catalog and content.

Read the deep-dive

Next step

Take the readiness assessment.

Twenty-five questions across five sections, about fifteen minutes. You receive four independent outputs — Organizational Readiness, Migration Complexity, a Platform-Fit Signal indicating whether your workload leans Snowflake, Databricks, or both, and AI Foundation Readiness from the data-foundation questions AI projects depend on.

Start assessment Talk to us