Manufacturing
Your factory’s everyday records could earn you extra money.
Every shift, your team checks parts, fixes machines and sorts out problems on the line. The records of that work show how real factories keep production running.
No files to upload. No commitment to sell.
On a normal day in a factory, jobs are planned, the line runs, parts are checked, a machine stops and an engineer finds out why. A supervisor decides what happens to a doubtful batch. All of this ends up in job sheets, check forms, maintenance logbooks and shift handover notes.
By records, we simply mean the information your company keeps while doing its work. In a factory, they are a running account of what went wrong, what your people decided and whether the fix held.
That is something AI companies find hard to get. Manuals explain how a process should run. Your records show how it really runs with a worn tool and a late delivery, and how experienced people put it right.
Which factory records could be worth money — and why
Quality checks
A quality check on its own is just a measurement. It becomes useful when it is linked to what happened next: the batch was held, the cause was found, the parts were reworked or scrapped. That chain shows how a team judges whether something is good enough.
Machine repair and maintenance logs
A good repair record says which machine stopped, what the engineer saw, what was tried first and what finally worked. Over years, these logs show repeat faults and their real causes, which can help AI learn to tell a symptom from a cause.
Scrap and rework notes
Rework notes explain why a part was rejected and how it was put right. The reason matters far more than the count. “Rejected — burr on edge, deburred, passed” teaches much more than a tally of rejects.
Job sheets and supplier problems
Job sheets become useful when they record changes — a job moved to another line, a run cut short, a setting adjusted — with the reason. Supplier problems work the same way. A note on whether you waited, switched supplier or changed the schedule, and how it turned out, shows how a factory weighs cost, time and risk.
Records that are usually less useful
Bare production totals, machine counters with no notes and copies of standard forms say what happened but not why. Records that stop before the outcome is known are weaker too. And not every record can be sold: some belong to your customers, and some you promised to keep private.
Three examples from businesses like yours
The three examples below are illustrative. Each is a short, complete story: problem, decision, result.
A batch failed its quality check
A quality check found that a batch of metal brackets was slightly too thick.
The supervisor stopped the line and the engineer traced it to a worn cutting tool. The tool was replaced, the machine was reset and the batch was checked again.
The next batch passed. The team noted the tool’s age so it is replaced earlier next time.
Why it’s useful
A record of those steps can help AI learn how real production problems are found and fixed.
We check what you own and are allowed to share. Private details and restricted information stay out.
A machine kept stopping. The cause was found.
A press stopped three times in one week.
Engineer traced it to a worn hydraulic seal.
Seal replaced. No stops for six months.
Why it’s useful
This follows a repeat breakdown to its real cause. It can help AI learn to spot patterns in faults instead of fixing the same symptom again and again.
We check what you own and are allowed to share. Private details and restricted information stay out.
Wrong material arrived. Production kept going.
Delivered steel was the wrong thickness.
Buyer switched to a backup supplier for one week.
Orders shipped on time. Supplier was warned.
Why it’s useful
This shows how a team protects production when a supplier gets it wrong. It can help AI learn to weigh cost, time and risk when plans change.
We check what you own and are allowed to share. Private details and restricted information stay out.
What stays private in a factory
Many manufacturers make parts to someone else’s design. Those drawings and recipes belong to your customer, and passing them on would break their trust and often your contract. Drawings or customer details you promised to keep secret stay out.
Some customers ask you to sign a confidentiality agreement — a written promise to keep a client’s information secret. You may know it as an NDA. Anything covered by one, or that belongs to someone else, stays out unless they agree.
Staff names in repair logs and shift notes are removed or replaced, but that is not enough on its own. Even without a name, a note about the only night-shift setter on a particular line can still point to one person. A part number or an unusual size can likewise reveal which customer an order was for. Records like these are changed further or left out entirely.
Our AI-assisted check flags possible restrictions, such as a customer name in a repair note. Appropriate people then review rights and privacy. It is not an automated legal approval, and you approve what is included before anything leaves your hands.
Your records. Your choice. You approve the package before it is shared.
How we protect your informationSell once, or earn every year
Two separate offers. You can choose one, both or neither.
Is this right for your business?
A good fit
Factories that have kept quality, repair and rework records for several years, with some notes on why things happened. Contract manufacturers can fit well too: the customer’s design stays out, but how your team checks and fixes things is often yours to share.
Probably not a fit (yet)
If your records are mostly totals with no reasons, or nearly everything is covered by strict customer secrecy terms, there may be little to offer today. Data Farming could change that.
What to have ready
A rough list of where your quality, repair and job records are kept, and how far back they go. A note of which customers’ drawings or recipes you promised to keep secret. And one person who knows the records.
Manufacturing:
common questions.
No technical background needed.
We make parts to our customers’ designs. Can we still take part?
Often, yes. The designs stay out. Records of how your team checks, repairs and fixes problems may still be useful, as long as they do not reveal the design or break your agreement.
Most of our records are on paper. Is that a problem?
Not necessarily. Tell us what you keep and how far back it goes. We will tell you honestly whether it is worth preparing.
Will this slow down our production?
No. Selling existing records uses what you already have. With Data Farming, any changes are agreed with you first and kept small.
Do our own machine settings and process know-how have to be included?
No. You decide what is included, and many owners keep their own process secrets out. Settings that came from a customer, or are covered by a confidentiality agreement, stay out in any case.
Will our customers be recognisable in what is shared?
We work to prevent that. Records that could point to a customer, even without a name, are changed or left out. You approve what is included before it is shared.
Find out what your records could be worth.
Start with a short company assessment.