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Use case

E-commerce analytics warehouse

Build an ecommerce data warehouse across orders, GMV, inventory, and channels. OptimaFlo unifies your store, GA4, and ad data for ecommerce analytics without a BI team.

Last updated Monday, Aug 10, 2026

Ecommerce data is scattered by default: orders live in your store platform, traffic and channel data live in GA4, inventory lives somewhere else again, and GMV means something slightly different in each. OptimaFlo builds the ecommerce data warehouse that ties those together, so "what is our GMV by channel this month" has one answer instead of three spreadsheets that disagree.

Where the numbers stop matching

  • "Our GMV in the store dashboard does not match what marketing reports from GA4."
  • "We find out about a stockout from a customer complaint, not from the data."
  • "Every channel add means someone manually rebuilding the revenue rollup."
  • "Nobody has time to reconcile refunds and returns into the 'real' revenue number."

What the AI data team builds across orders, traffic, and inventory

The Ingestion Engineer connects your store's order data and inventory data, through whichever of OptimaFlo's database or REST API connectors matches your platform, alongside the built-in GA4 connector for traffic and channel data. GA4 spreads traffic source across several different dimensions depending on scope (first-touch, session, or event), with no single session table to query directly. OptimaFlo's GA4 connector validates every dimension and metric you configure against your property's live metadata catalog before the first row lands, and the Analytics Engineer models one canonical Clean-tier channel table from it, so you are not re-deriving the same attribution logic in every report.

The Data Engineer builds the pipeline connecting orders, inventory, and channel data into Ready-tier tables for GMV, order volume, and stock levels. The BI Developer turns that into a dashboard you can hand to the rest of the team, and the Quality Engineer attaches checks (a negative inventory count, a GMV rollup that does not reconcile with order count) so a bad number gets caught before it reaches a stakeholder.

Starting the stack, not migrating one

This is meant to be your first real analytics layer, not a rebuild of one you already trust. Read how the AI data team works end to end, or see the full role lineup that ships with every plan.

Standard is $50,000 a year for the full team. Cloud compute and your LLM key bill separately, at your provider's cost.

Roles on this use case

ManagerIngestion EngineerData EngineerAnalytics EngineerBI DeveloperQuality Engineer

Frequently asked questions

Yes. The Ingestion Engineer connects your store data (through a database or REST API connector, depending on your platform) alongside the built-in GA4 connector, and the Data Engineer models both into a single Clean and Ready layer instead of two disconnected reports.

The Data Engineer builds the aggregation logic once, in SQL you can read, so GMV rolls up consistently whether the order came from your storefront, a marketplace feed, or another channel you connect. The Analytics Engineer checks that logic before it runs, and re-runs it as new channels get added.

If your inventory data is connected (through a database or REST API connector), the Data Engineer can join it with order data in the same pipeline, so a stockout shows up next to the demand that caused it instead of living in a separate system nobody cross-references.

The BI Developer builds and edits the dashboard: chart types, layout, and live queries. You refine it by describing what you want changed, the same conversational flow as the rest of the team.

Standard is $50,000 a year and includes all seven roles. Cloud compute and your own LLM key bill separately, at cost.

See the whole team in action on the AI data team overview or browse every use case.

Staffed, not self-serve

See your AI data team work on your own data.

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Now in early beta. One flat plan, no per-query tax. Runs in your cloud, and your tables stay in your account.