Adolph Menzel · The Iron Rolling Mill · 1875
AI in Manufacturing
Ask most people about AI in manufacturing and they picture the floor. Robots, cameras, a lights-out plant. That work is real, but it is rarely where the time and money go. The cost is upstairs, in the quoting, the planning, and the decisions that still run on one senior person and a spreadsheet.
That is the work AI is getting good at. Reading a spec, pricing a job from history, planning around the capacity you actually have, answering questions that used to need thirty years on the floor. The manufacturers pulling ahead are building it into those steps, so the operation stops depending on who is in the room.
Solutions
These came out of our engagements with manufacturers. Nothing here is software you log into. Each one gets rebuilt on your own parts, routings and job history.
An RFQ lands with prints, specs and a due date, and your best estimator disappears into it for two days. This reads the whole package, pulls out every part, and prices each one against jobs you have already run, at your work centers and your cycle times. The estimator checks the number instead of building it.
Quotes go out in hours.
A submission is four documents that all have to tell the same story about the same characteristic, and roughly 60% of rejections happen because one of them quietly does not. This balloons the drawing, drafts the PFMEA and the control plan off your own defect history, then runs the customer’s trace before the customer gets to. Your engineer approves every step.
Submission ready in a day.
You send one package out for bar stock, castings, heat treat, grinding and plating, and six suppliers answer in six different shapes. Somebody rebuilds all of it into a spreadsheet by hand and hopes nothing slipped. This reads whatever they send, lines it up item by item, flags the best price on every line, and calls out the lines only one supplier bothered to quote.
One comparison sheet you can trust.
The schedule lives in one spreadsheet and one person’s head, so every promise date is a guess until they are back at their desk. Give this the open orders and the capacity you actually have, and it lays out the week, shows you where two jobs collide on the same machine, and tells you whether Thursday holds.
You can promise a date on the call.
Your installed base calls in with the same questions, and the answers sit buried in service manuals and ten years of closed tickets. This reads all of it and answers the technician straight, with the manual section and the old ticket it pulled from sitting right next to the answer. Anything it has not seen before goes to a person.
The basics stop reaching your team.
We map how work moves through your operation and find the step everything else is waiting on. Then we build the fix, pulling from what is already above and writing what does not exist yet. Your team learns to run it and extend it, so the capability stays in the building after we go.
Built out of the five above.
If one of these sounds like your operation, or your problem is not on this list, book a call and we will tell you straight.
Book a CallInsights
We write up what we find inside manufacturing operations. Each briefing takes one process apart, shows where the time and the money actually go, and sets out what changes when AI is built into that step. Free to read, and written for the people who run it.
Estimating and quoting
Why quoting still runs on spreadsheets and email
How an estimate actually gets built inside a discrete manufacturer, why quoting software built on CAD geometry misses most of the work, and what changes when every line is priced from jobs you have already run.
Quality and launch
Why quality approval still runs on filing cabinets
Why submissions get rejected on consistency rather than content, what the interconnected elements of a package have to agree on, and how the whole submission can be drafted, traced and checked before the customer ever opens it.
