A distributor that runs its
back office on AI.
A Western Australian wholesale food distributor runs day-to-day operations on a custom AI system. Not a chatbot bolted onto a website. An operational layer on top of the ERP and Microsoft 365 they already had.
The shape of it
Two halves on top of the systems they already had.
The workforce: 34 packaged workflows
Real recurring jobs, packaged and repeatable: apply supplier cost changes across staggered price tiers, process emailed orders into the ERP, reconcile every supplier invoice line by line against its purchase order, draft the monthly rebate claims, forecast demand, build per-store reports.
The workplace: a team dashboard
Roughly 49 pages the whole team logs into, with five-tier role-based access, two-factor auth and an AI assistant on every screen. The team approves price batches, activates promotions, works the retention list and runs the warehouse pick queue from it.
Before
- Pricing maintained by hand across wholesale tiers and RRPs, drifting in spreadsheets
- Orders arriving as forwarded emails, PDFs and spreadsheets, each retyped into the ERP
- Supplier invoices eyeballed against purchase orders, or skipped when time ran out
- Weekly and monthly reporting assembled by hand
- Rebate claims filed late or not at all. Unfiled claims are margin left on the table
After
- Price lists, cost changes, reports, statements and claims run on their own schedule
- 3,361 successful runs against 10 failures over a representative fortnight
- Every invoice diffed line by line. Every claim drafted on cadence
- Churn risk, stock problems and cost drift surface early instead of late
- The team approves, the system does the typing
The real headline
The biggest wins were leakage, not speed.
The most valuable jobs were the ones quietly being skipped because they were too tedious to do consistently: checking every supplier invoice, filing every rebate claim, keeping related products on the same margin logic. Automating those recovers money, not just hours.
On top of that sits capability that did not exist before at all: per-store analysis, churn scoring, demand forecasting and competitor intelligence that nobody had the hours to do, now running on a cadence.
Why it is safe
The hard part was never the AI.
The AI never writes to the live database directly. It works through purpose-built capability layers that expose narrow, read-only-by-default operations. Every write to live data goes through a reviewed process that runs dry-run first, shows its output, and executes only on explicit human approval, with everything logged to an audit trail.
The genuinely difficult engineering was this boundary layer, not the AI. That ordering is why it works in production instead of being a demo.
Honest limitations
- It is supervised, not autonomous. A human approves every consequential write and every external email
- The 0.3% of scheduled runs that fail still need a person to notice and fix them
- A few dashboard areas are still placeholders, marked as such
- The time-saved figure is the operator's estimate, not a stopwatch study
The pattern that generalises
The domain changes. The shape does not.
- Wrap the systems you already have. No rip-and-replace, no migration project
- Find the recurring admin and put it on a schedule
- Hunt the leakage, not just the time
- Gate every write, log everything, dry-run first
- Package it so a non-technical team can use it
If your business runs on an ERP, has a mailbox full of orders and invoices, and a person spending afternoons on admin that follows the same steps every time, this pattern applies to you.