— Operations Manager
You opened Shopify this morning. Checked orders. Glanced at revenue. Scrolled inventory. Looked at returns. Nothing actually told you what’s going on.
Freddy does that in the background. Every day.
Four operational checks run every morning before you wake up. One watches revenue. One watches inventory. One watches fulfillment. One watches returns.
Wednesday, January 29
Revenue: $4,847 — up 51% from yesterday.
AOV $89. 54 orders. Best day this week.
Mountain Jacket selling 4x faster than last week.
~6 days of stock left. Might want to reorder.
3 orders unfulfilled past 48 hours.
#4809 is 5 days old. No staff notes.
Slim Fit Chinos — 4 returns this week.
All cite “sizing”. Third week in a row.
That’s your morning. Talk tomorrow.
— Freddy
Not a dashboard. A briefing.
Not a chart. A sentence.
Every order from the last 24 hours, compared to your prior 30-day daily average. Top products ranked by what actually sold. Discount code usage and what it’s costing you.
“Revenue is up 51% — Mountain Jacket sold 18 units, 4x its 30-day daily average.”
Not how many you have. When you run out.
Freddy calculates daily demand and days-of-supply for every product. Under 7 days is critical. Zero sales in 30 days is dead stock — and Freddy tells you exactly how much capital is tied up in it.
Stockout risk
Mountain Jacket — Navy: daily demand 2.6 units. 16 left. 6 days of supply.
Dead stock
Linen Shorts — Beige: 0 sales in 30 days. 62 units sitting. $1,860 tied up.
Before the customer emails.
Time from order placed to first shipment. Slowest orders. Full backlog sweep of anything unfulfilled past 48 hours, grouped by age so you can see it getting worse.
If something looks stuck, Freddy checks the timeline — staff notes, fulfillment events, status changes. Partially fulfilled because one item is held up? You’ll see exactly which part is waiting.
One return is a return. Four is a pattern.
Freddy pulls every refund. Groups them by product. Reads the return reasons and customer notes. Ranks the top offenders.
“Slim Fit Chinos — 4 returns this week, all cite sizing.” That’s not a refund problem. That’s a product listing problem. And now you know.
It doesn’t just report. It investigates.
Deterministic data pipelines scan every order, variant, and return. Fulfillment backlogs include timeline context for stuck orders. Refund snapshots surface recurring issues automatically.
By the time you read the report, the structured checks have already been run and aggregated.
Soon, it watches your ads too.
Google Ads. Facebook Ads. Same approach — not dashboards, but actual analysis.
“Product X is about to sell out. Reduce ad spend and reuse the winning creative for Product Y.”
ROAS only sees the ad. Freddy sees the business behind it.