AI that catches the small things your team misses.
The invoice that does not match the order. The emailed order someone keys in by hand. The answer that is in a procedure nobody can find. I find where the hours and the errors actually go, put one AI job inside the workflow where that work already happens, and prove it against the baseline before it touches anything live.
Twenty minutes, bring one problem. Onsite across Melbourne, remote for the rest.
Call me when…
- Someone matches supplier invoices to purchase orders by hand, and the mismatches are found at month-end or not at all.
- Orders arrive by email and as PDFs and are keyed into the ERP by hand, and the typos reach your customers as wrong deliveries and credit notes.
- Your ERP, your web store and your 3PL disagree about stock or price, and someone reconciles them in a spreadsheet every week.
- Staff hunt through procedures, specifications and old emails for an answer that is written down somewhere.
- The board has asked what you are doing about AI, and the honest answer is a few experiments and no measured result.
- You ran an AI pilot. It worked in the demo and never reached daily use.
What I do
Exceptions found before they cost money
The small differences a busy team stops seeing.
- Supplier invoices read against the purchase order and the receipt: price, quantity, unit of measure and terms, with the difference and the likely cause on each exception.
- Stock and reorder exceptions: the lines where the forecast, the sales history and the current order do not agree, ranked by what they cost.
- Product and price data checked before the document goes out: GTINs, pack hierarchy, GLNs, prices and dates against the retailer's rules.
- Every exception carries its evidence. Nothing changes in the live system without a person's approval.
Orders and files that do not need re-typing
The hours spent moving numbers from one place to another.
- Emailed and PDF purchase orders read into a sales-order draft: customer, ship-to, lines, quantities and dates, with every uncertain field flagged.
- Supplier price lists and cost updates in any layout read into your load template, with the exception report before the load.
- Forms, claims and returns turned into structured records for the person who handles them.
- The draft is reviewed and posted by your staff. The AI fills; it does not post.
Answers with the source attached
The knowledge that is written down and still hard to find.
- Your procedures, specifications, retailer guides and contracts searched in plain language.
- Every answer cites the document and the section it came from. No citation, no answer.
- Scoped to your documents in your accounts. Nothing is sent to train anyone's model.
- In regulated production the search is read-only and validated with your quality team.
The five steps
Every engagement runs the same five steps, in this order. Each one has its own article.
Find where the hours and the errors actually are
Two weeks of real work, counted: minutes per task, errors per hundred, what each one costs. The ranked list is the plan. Read the step →
Pick one job, not twelve
One task with a clear input, a clear output and a measured baseline. If a rule does the job, it is not an AI job. Read the step →
Prove it against the baseline, not in a demo
Same work, same people, a few weeks, numbers before and after. Kept if it beats the baseline, stopped if it does not. Read the step →
Put it inside the workflow
In the ERP screen, the inbox or the document flow where the work already happens, not a separate tool someone has to remember to open. Read the step →
Keep it honest
A person approves every change to live data, every output is traceable to its input, and changes go through your change control. Read the step →
Three ways to start
AI opportunity check · a few days
- What you get
- A ranked list of the tasks where hours and errors concentrate, with minutes, error rates and what each costs; which of them an AI job fits and which a rule or a report fixes; and the first pilot scoped on one page: input, output, baseline, how it will be judged.
- How you check it
- Every number in the list comes from your own records and your own people, and you can recount it.
Pilot · a few weeks
- What you get
- One AI job built where the work already happens, run on real work beside the current process, with the baseline and the result measured the same way. A written verdict: keep, change or stop. Your data stays in your accounts.
- How you check it
- The before and after numbers, counted the same way, in your own system.
Rollout and support · by the day
- What you get
- The proven job taken into daily use: the approval step, the audit trail, the change record, the staff trained, and support by the day when it does something odd. A written cap and a weekly note; stop at the end of any week.
- How you check it
- The written cap and the weekly note.
No price is printed here. The shape and the rate are said on the first call and written down before any work starts.
Safeguards
- A person approves every change to live data. The AI drafts, flags and ranks; your staff post.
- Your data stays in your accounts and is not used to train anyone's model.
- Every job is measured against a baseline before and after, counted the same way.
- In pharmaceutical and other regulated production, the work is validated with your quality team against GMP and the TGA's rules, with a data-integrity record from the start.
- No AI where a rule does the job. A validation rule, a report or a lookup is cheaper, faster and easier to audit.
Where this applies
- Wholesale and distribution: supplier invoices, emailed orders, price files, stock exceptions.
- Manufacturing: reorder and shortage exceptions, specifications and procedures, supplier documents.
- Pharmaceutical and regulated production: procedure and deviation search, batch-record checks, all under GMP.
- Suppliers to retail: product and price data checked before the EDI document leaves.
Industries where the work fits. Not a client list.
Contact
Seabrook, Melbourne