Coverline Install on Shopify

Inventory operations, on autopilot

Every SKU, watched. Purchase order, drafted before you run out.

Nightly forecasts on every variant.
A reorder queue ranked by days of cover
so you act early, not urgently.

Install on Shopify See how it works

Reorder queue

Last updated 2h ago Export CSV
Status SKU Product variant Days of cover Order by Order qty Revenue at risk
At risk CL-TEE-BLK-S Coverline Tee – Black / S −3d May 14 240 $18,240.00
At risk CL-HOOD-BLK-M Coverline Hood – Black / M −1d May 16 120 $15,120.00
At risk CL-TEE-WHT-M Coverline Tee – White / M 0d May 17 200 $12,800.00
At risk CL-CAP-BLK-OS Coverline Cap – Black / OS 1d May 18 180 $7,200.00
On track CL-HOOD-GRN-L Coverline Hood – Green / L 2d May 19 100 $10,900.00
On track CL-TEE-BLK-L Coverline Tee – Black / L 3d May 20 160 $10,240.00
On track CL-TEE-WHT-S Coverline Tee – White / S 5d May 22 220 $8,360.00
On track CL-SOCK-WHT-OS Coverline Sock – White / OS 6d May 18 300 $4,050.00
On track CL-HAT-NAV-OS Coverline Hat – Navy / OS 7d May 19 140 $3,920.00
On track CL-HOOD-BLK-XL Coverline Hood – Black / XL 9d May 26 80 $6,720.00
On track CL-TEE-GRN-M Coverline Tee – Green / M 12d May 29 160 $5,760.00
On track CL-TEE-NAV-L Coverline Tee – Navy / L 15d Jun 1 120 $4,560.00
Showing 1–12 of 52 at-risk SKUs View all queue
An illustration of Coverline’s reorder queue using sample data for a fictional apparel catalog. The figures are not from a real store.

The problem

Running out of
the item that sells.

Late orders. Lost revenue. Disappointed customers. All from gaps you could see coming.

The other problem

Cash buried in
the item that does not.

Too much stock. Slow turns. Margin erosion. All from guesses, not signal.

How it works

One loop, run every night, without you.

Coverline does not add a dashboard to check. It runs after your day closes and leaves a queue and a drafted order waiting for you.

  1. Forecast

    Every variant gets a fresh demand forecast each night, on your store’s own clock, after your day has closed.

  2. Reorder point

    Lead time, review period and your service level turn that forecast into the day each SKU has to be ordered.

  3. Drafted PO

    Everything due is grouped by supplier and priced into a purchase order, respecting MOQs and case packs.

  4. Weekly digest

    Monday morning, one email listing what needs ordering — for the weeks you never open the app.

Why the numbers hold up

Safety stock is read, not guessed.

  • A quantile forecast, not a single number. A 95% service level is the 95th-percentile forecast over the lead time. The distribution is the product; a point estimate would leave you guessing again.
  • No per-SKU training. The forecaster is zero-shot, so a SKU that is three weeks old gets a forecast on day one instead of waiting a season to earn one.
  • Stockout days are repaired before they are read. A zero logged while you were out of stock is not demand. Left uncorrected it teaches the forecast that demand fell, which causes the next stockout.
  • Every SKU is routed to whatever wins on its own history. The model is measured against plain baselines on held-out data per SKU, and loses the ones it does not beat.
Daily demand for one SKU, then a 28-day forecast. The shaded band is the 10th to 90th percentile of the forecast distribution, widening with the horizon. OBSERVED FORECAST, P10–P90
One SKU · 56 days observed · 28 days ahead · band widens as √h

Pricing

Priced on the SKUs we forecast for you.

Not on orders, and not by withholding features. Every plan runs the whole loop.

Starter

$29/month

Up to 250 SKUs

For a focused catalog — one brand, a few hundred variants.

Start free trial

Growth Recommended

$79/month

Up to 2,000 SKUs

For a full range across sizes and colourways.

Start free trial

Scale

$199/month

Unlimited SKUs

Every SKU you carry, however many that is.

Start free trial

14-day free trial on every plan. Billed monthly through Shopify and charged to your Shopify invoice. Cancel any time — your history stays put.

Privacy

We store no customer data. None.

Order ingest keeps only what a forecast needs. There is no customer table in the schema and no personal field anywhere in it, which is what makes the mandatory GDPR webhooks provable no-ops rather than a policy promise.

Read the privacy policy

  • Order id
  • Date
  • Variant id
  • Quantity
  • Line total
  • Names
  • Emails
  • Addresses
  • Phone numbers
  • Payment details