From Google Sheets to thousands of orders a day, with cancellations down ~35%.
We close the gap between the data you have and the decisions it should be improving. A small senior team sits in your stack, gets the numbers agreeing, builds the systems and agents that act on them, and stays until your team runs them without us, with no migrations and no new platforms.
A hardware business scaling on a spreadsheet that broke a little every day.
Every order needed a survey approved, a delivery scheduled with the carrier, an install confirmed, a serial number logged, and a subscription activated. Each step was a person copying between Shopify, the sheet, email, and the carrier portal, and every gap in the sheet was a customer not hearing anything.
| Order | Survey | Approved | Email sent | Sent to carrier | Install date | Serial | Subscription |
|---|---|---|---|---|---|---|---|
| #37…2559 | Complete | Yes | 4/29 | Yes | 5/13 | AMP1S…0096 | Active |
| #37…1534 | Complete | Pending 29 days | Not found | ||||
| #37…6617 | Complete | Yes | 5/8 | No date | Rescheduled 3x | AMP1S…0110 | Waiting |
| #37…2084 | Resubmitted | Duplicate row | 5/9 | Yes | 5/27 | Mismatch | Active |
| #37…9706 | No data | Sent anyway | Unknown | Not found |
One order flow across all four systems, and a team that only touches exceptions.
The systems already knew everything about each order; nobody had wired them together. We built the integration layer between Shopify, NetSuite, the 3PL and Salesforce: order, inventory and status events sync automatically, one record carries the truth, and a delivery date reaches the customer without anyone opening a sheet.
The volume tripled through the same team, and customers stopped cancelling in the silence.
The automation was the enabler; these are the outcomes it bought:
| Measure | Sheets era | On the backbone |
|---|---|---|
| Cancellation rate | ~8% | ~5% |
| Orders handled per day | Hundreds, with overtime | Thousands, same team |
| Order-to-scheduled-install | ~9 days | ~4 days |
| Orders touched by hand | All of them | Exceptions only |
What held, what needed rework.
The backbone carried peak season without added headcount, and the exception queue stayed the whole job.
Early exception rules were too cautious and queued too much for human review. Thresholds were retuned once real volume showed where judgment actually mattered.
The engagement grew from fulfillment into the metric layer and governed AI self-serve, on the same embedded model.
Get in touch.
Tell us what you are trying to figure out. If we can help, we will say how. If we are not the right fit, we will say that too, and point you somewhere better.