Custom Software and AI for Manufacturers

The real problem is the gap between the floor and the ERP

The ERP knows what was ordered, built and shipped. The machines, and the MES if you have one, know what happened on the line. Between the two sit the clipboards, whiteboards and spreadsheets where the plant actually records inspections, downtime, scrap and rework. Every one of those is retyped at least once, and some are never entered at all.

That gap is where most of the value is, and none of it needs AI to capture. The first project we would scope for most plants is plain software: a tablet form at the station instead of a paper traveler, a nightly sync that moves counts and scrap into the ERP, a dashboard the shift lead can read without running a report.

Quality and maintenance records people will fill in

Quality and maintenance systems fail for the same reason: the record is harder to create than the work it describes, so it gets created late, from memory, or not at all. We build for the operator and the technician first.

• →Nonconformance and CAPA. A nonconformance logged at the station with the part number, lot and photo attached, routed to the right engineer, and tracked through to a corrective action that is actually closed.
• →Maintenance logs. Work orders opened from the machine, with the fault, the fix and the parts used captured in a few taps, so the history exists when someone needs it.
• →Work instructions. Current, versioned instructions at the station, with the revision the operator used recorded against the job.
• →Supplier documents. Certificates of conformance and material certs read on arrival, matched to the purchase order and lot, and flagged when something is missing or does not match. This is a good first use of AI: extraction from documents. Depending on the volume and the risk, the extracted values can be checked in code against the purchase order and lot record, with anything that does not match held for quality staff to confirm.

What has to be true before AI inspection or predictive maintenance works

Both are real, and both are sold far more often than they are ready. What each one needs depends on which version you mean.

AI visual inspection always needs images captured under the lighting and camera position the system will use in production. From there, two approaches. Anomaly detection learns what a good part looks like from images of good parts and flags anything that differs, which suits defects that are too rare to collect many examples of. Classifying defects by type (scratch, void, misalignment) needs labeled examples of each type, labeled by someone who knows the difference. Either way, a flagged part goes to an inspector, and the plant decides in advance what happens next.

Predictive maintenance also comes in two kinds. Condition monitoring learns the normal pattern of a machine's sensor readings (vibration, temperature, current, cycle counts) and raises an alert when they drift from it; that needs a sensor baseline from healthy running, not a history of failures. Predicting which failure is coming, or how long a component has left, does need a record of real failures on those machines and what caused them. Many plants have the sensors and not the failure log, which makes condition monitoring the realistic starting point.

If your plant has neither the images nor the sensor history yet, the first project is the one that starts collecting them, and the audit will recommend that rather than a model with nothing to learn from.

Working with the systems you already run

We do not replace an ERP or MES to add a workflow on top of it. We connect to what is there, through its API where it has one, through its database or file exports where it does not, and we confirm during the audit what each system actually allows before anything is scoped. Systems we have connected in production include accounting software such as QuickBooks, CRMs, email systems and clients' own internal systems.

Where a system is too old to connect to at all, that becomes a modernization question, and our legacy ERP workflow page covers how we approach it.

How a manufacturing engagement runs

We start with the 90-minute AI workflow audit: one session walking through how a job, an inspection or a breakdown actually moves through the plant, where the retyping and the missing records are, and which one is worth fixing first. The audit is complimentary when it is bundled with a larger build and available as a paid one-off on its own.

From that we write a priced scope for one workflow on one line or cell. Prototype, staging, and deployment milestones are agreed in the written scope. Before the build, you record today's number for the thing we are changing (hours spent entering inspection data, days from nonconformance to closed CAPA, share of work orders with a recorded cause), so the result can be checked against it. Once one line works, the same forms and integrations extend to the next.