AI consulting for job shops and small manufacturers

A job shop or a small production floor does not run like a plant with an IT department. Much of its week is paperwork: quotes, inspection reports, the maintenance binder, the note for the next shift. That paperwork is where we start. Our guide lists six jobs a small manufacturer can hand to AI first, with what each one needs and where it goes wrong.

We suggest RFQ intake as the first job, because RFQs arrive every day and one person already owns them. The AI reads the email and the drawing and fills in your quote checklist: material, quantity, finish, the tolerances called out, the delivery date and what the customer left out. It pulls your most similar past jobs from your quote history, so your estimator starts from real numbers. Then the estimator checks the checklist against the drawing and sets the price. AI misreads GD&T symbols, crowded tolerances and scanned drawings often enough that a person has to check every one. Our quote automation service sets out the steps from request to sent quote. No quote leaves until someone on your team has read and approved it.

Quality records work the same way. An inspector or operator photographs a defect and says one sentence about it into a phone. The AI fills in your nonconformance report with the part number, the job, the defect type, the quantity affected and where it was found. It files the photo under that job, so the next time the part runs someone can search what went wrong before. A phone photo is not a measurement. The AI can describe a scratch or a burr. It can't say whether a bore is in tolerance, so accept and reject calls stay with your gauges, your CMM and your quality lead.

A maintenance log has to be text before anyone can search it. If yours is handwritten, someone types the last twelve months into a sheet, one row per event. After that your maintenance lead can ask when the way covers on the second mill were last replaced, or how often the compressor has tripped this year, and the answer comes from your own entries. The log only knows what someone wrote down. A machine that gets fixed on night shift without a note has no history. Shift handover notes are how that note gets written. The outgoing lead dictates what ran, what stopped and what is waiting on material. The AI writes the handover in your format and adds each machine event to the log. The lead reads it before the next shift does.

We also build predictive maintenance and camera inspection on the line. We don't suggest either as a first project for a small shop. Predicting a failure needs months of sensor data, such as vibration, temperature and spindle load. If your machines already record that, it can be a later step, once the paperwork jobs run without anyone thinking about them. A searchable log tells you what happened. It can't tell you what is about to happen. Camera inspection needs fixed lighting and a model trained on your parts, which makes it a bigger build than writing up phone photos.

A first project starts with a call. We ask which of these jobs comes up every day and already has an owner, and we pick one. Before anything gets built, we look at an export of the records it would read, such as three months of quotes, emails or job lists. If the export is a mess, cleaning it up is the first project. We agree on the scope with you before work starts. The system starts in draft mode, where the AI drafts and a person approves every output. Then we show your team how to run it.