Built by operators, for operators

Cut the fat out of how your business runs.

We find the hours and dollars your operation loses to manual work, then build the exact software that gets them back. Built by people who run businesses, not software companies guessing at your problems.

$tell us where it hurtsrun the drag check ↓

The problem, priced

Your business carries fat you can't see. Price yours.

nine switches. sixty seconds. no email. flip every row that sounds like your week.

typical figures, from real builds

faults flagged: 00
flagged something? watch the engine that clears it ↓

Watch an engine run

Three engines, live on screen. This is the actual product, with example numbers.

live demosexample data
$ build report --daily
net_sales$47,832
orders412 (avg $116)
margin38.2%
vs_last_week+12.4%
top_locationHenderson
flagPM shift understaffed
✓ delivered 6:30 AM, before anyone walks in

that is one engine. here is how deep they go ↓

How deep an engine goes

This is how deep an engine goes when buying is your biggest line.

// if you buy inventory from vendors, five levers sit between you and a leaner operation: orders sized by math, cash split by urgency, terms backed by your own shelf, suppliers graded, dead capital named. one engine pulls all five.

purchasing_intelligence · five leversexample data

Orders sized by sell-through, not gut

suggested = velocity × wos_target + lead_cover − on_hand → rounded to cases

skuvelocityon_handordercheck
sku_021418/wk224 cscase of 12
sku_10429/wk63 cslead 14d
sku_058831/wk406 csMOQ 24 met
sku_22045/wk32 csstore b +1

$ create PO

✓ 7 formatted order sheets written, one per location. hours of buyer work, one click.

// every suggested order is computed, case-rounded, MOQ-aware, split per store. the math is on screen because you should never trust a number you can't check. then one click on create PO turns the whole buy into formatted order sheets, one per location.
from a live deployment. our own stores, every day. not a promise of yours.
Live screen from our purchasing engine deployment: brand power rankings with grades, scores, margins, and momentum. Brand names and dollar columns redacted.

live screen, this morning's sync · brands and dollars redacted

SKUs livebrands managedlocationsmonthly float surfacedstockout risks pre-drafted

every engine starts the same way ↓

How it works

01

Diagnose

Where your hours go, where your handoffs break, where the drag is. We learn your operation before we touch anything.

02

Build

The exact tool that removes your drag, fit to your operation, not the other way around.

03

Run

The engine takes the job. Your team gets the hours back and your numbers stay right, every day, on their own.

we build working software. AI is a tool we reach for when it is the right one for the job, never the pitch.

we ran this playbook on our own stores first ↓

Our own numbers

From cannabis retail and cultivation, one of the hardest categories in retail.

regulated, margin-thin, manual everything. our own multi-location operation, run lean by engines we built on the floor: purchasing_intelligence · executive_intelligence · bonus_engine · daily_ops_report · compliance_reporting

minutes of daily reporting, per store, per manager
quarterly compliance filing, zero resubmissions
violations across years of regulated operation

✓ hard mode, made lean. read the full case study →

that was us. is it you? ↓

Is this you?

Operators buried in process.

  • regulated industry, paperwork that never stops
  • multi-location, outgrew the spreadsheets years ago
  • small-to-midmarket team doing enterprise process by hand
  • your week disappears into numbers you only half believe

sounds familiar? one step left ↓

One clear action

Tell us where it hurts.

$book a demo

30 minutes. no pitch deck. bring your drag profile, or just the process your team hates the most.