See changes in order patterns
Use existing customer and order history to identify accounts whose buying rhythm changed. A salesperson checks the reason before contacting anyone.
Read the lapsed-account case →
Manufacturing · Ontario
Practical AI for manufacturing, built around the systems you already run.
I help manufacturers find useful work in the orders, quotes and reports they already handle. That might mean spotting customers who stopped buying, cutting repetitive entry, or giving managers a current view of the business. We test one question against approved data and keep a person in charge of the decision.
One manufacturer · order history
264 / 804
lapsed accounts surfaced
From six years of ERP orders. Accounts found, not revenue recovered.
Where to look first
Three useful manufacturing jobs.
These are patterns from delivered work, not a promise that every plant needs the same tool.
Reduce quoting re-entry
In a separate project, a quote workflow brought intake and field requests into one place and reduced repeated typing. The source systems and approval steps still matter.
See the quoting proof →Make reporting usable
For one Infor VISUAL manufacturer, a live KPI dashboard replaced a manual Excel rebuild. The method depended on that client's access and data setup.
Read the KPI case →
Keep the source of truth
Your ERP stays in charge.
A manufacturer can start with an approved read of selected ERP data, test an output against known records, and let the right person act on it. Replacing the ERP is not a prerequisite for every useful AI or reporting project.
The read-only API described in my Infor VISUAL work was built for one installation. I check each company's version, permissions and integration options before proposing an access path. If a plain report or rule solves the problem better than AI, that is the right starting point.
- 01 · QuestionName the decision and who will use the result.
- 02 · DataConfirm permission and compare source records.
- 03 · TestCheck a small output against known examples.
- 04 · HandoverTrain the users and agree on review and support.
Real engagements · names removed
What the work actually showed.
The customer and dashboard examples below come from the same Infor VISUAL manufacturer. The quoting example is a separate engagement. None of these figures is a typical result or a forecast for your business.
Customer visibility
Six years of order history were used to classify 804 accounts and surface 264 lapsed accounts. This identified follow-up candidates; it did not establish recovered revenue.
How the accounts were found →Live reporting
The same manufacturer's VISUAL data fed a live KPI dashboard. Its team reported at least three hours a week of manual reporting removed.
How the dashboard worked →Quote workflow
A separate manufacturing engagement put intake and field requests into one quote workflow, with six hours a week back from quoting and invoicing work.
See the delivered work →You can also read how manufacturers use AI without replacing ERP for a more detailed implementation sequence.
Fit and limits
A useful build needs the right boundaries.
I start with one owner, one business question and approved data. The people who run the process should be able to check the output and tell me when it is wrong.
This work is about commercial and operational information. It does not claim machine control, predictive maintenance or plant safety capability.
- AccessWho can approve the data source and what may be read?
- QualityWhich known orders or reports can validate a first result?
- Human reviewWho will check customer, quoting or production decisions?
- OwnershipWho will use the output, maintain it and receive training?
One bottleneck first
Bring me the report, quote or customer question that keeps coming back.
We can discuss the smallest useful test in a free conversation. If it needs deeper discovery or a build, scope, fee, training and handover are agreed in writing first.
Serving Ontario from Ancaster. For a broader view beyond manufacturing, see AI consulting for Ontario businesses.