The problem

A group of consumer brands sells on Amazon, its own Shopify stores and a few smaller marketplaces. The numbers lived in a dozen places: three Amazon seller accounts, three Amazon ad accounts, three Google Ads accounts, three Shopify stores, QuickBooks and two Amazon warehouse programs.

Every morning someone had to log into each one to answer simple questions. How did we do yesterday? What is about to run out? Which channel is slipping? By the time the answers were pulled together, the day was half over.

What we built

Two layers. First, a pipeline that pulls everything into one place every morning without anyone touching it. Second, an AI that reads all of it, compares it to history and tells the team what matters.

The dashboard is the easy part. The value is the review.

How it works

  1. Pull the data. Three scheduled runs start before 7 AM: sales first, then ad spend, then inventory and finance. Each account is stored separately, so nothing is ever mixed.
  2. Sales by day and by SKU for every Amazon account and every Shopify store, on one consistent day boundary.
  3. Ad spend and attributed sales for every Amazon and Google Ads account.
  4. Inventory by SKU from Amazon fulfillment, Amazon's warehouse program and QuickBooks, snapshotted daily so there is history to compare against.
  5. Finance transactions from Amazon, so fees and charges are visible, not buried in a settlement report.
  6. Catch what failed. A retry run at 7:25 picks up anything that did not load and logs the exact error. A weekly data quality check runs in the background.
  7. AI review. Once the data lands, the AI reads it against the history in the database and writes a short summary: yesterday against the same day last year, trailing 7, 30 and 365 days, by brand and channel.
  8. Suggestions, not just numbers. It flags SKUs whose sales pace will run them out before stock can be replenished, channels or ad accounts that are underperforming their own trend, and anything that broke overnight.
  9. Deliver. At 8 AM the partners get an email with the headline numbers, the AI's notes and a link to the live dashboard, already updated.

What changed

Nobody logs into a dozen systems anymore. The morning question is answered before the team sits down, with the reasoning attached.

The same database now feeds work that used to take days: a month-end inventory valuation by location with landed cost on every line, and the monthly sales tax worksheet. Because inventory is snapshotted daily, stockout calls are based on real sales pace, not a guess.

What we would do again

Our take: a dashboard nobody opens is worthless. Put an AI between the data and the people, and have it say what changed and what to do about it.

Store everything, by account, every day. History is what lets the AI judge whether a slow day is noise or a trend.

Judge on longer windows. A five-day dip is noise. The AI compares against 30 and 365 days before it calls anything a problem.

Build the retry in from day one. APIs fail. A pipeline that logs the exact error and tries again is one people can trust at 8 AM.