Maintenance intelligence for manufacturers
An AI business application, built in 90 days, that helps manufacturers understand recurring faults and resolve them faster using how similar faults were fixed before.
From brief to a working AI business application.
Information recall lifted above 95% by RAG within 3 months.
An automated root-cause workflow that records each fix.
The answer was in the history. Nobody could find it in time.
Manufacturers lose hours when the same faults keep coming back.
The fix for a recurring problem often already existed in an old work order, a machine manual or a senior technician’s head. It rarely reached the person standing at the machine.
Fixes were recorded inconsistently, so the next technician had little to go on. Each similar job risked becoming a fresh investigation, and the wrong fix could be repeated.
A maintenance SaaS business serving manufacturers.
Their customers are operators and maintenance teams who need to understand recurring faults, resolve them faster and keep lines running. The business wanted AI that made their product noticeably more useful on the floor, not a demo.
Client name withheld.
An AI application that remembers what worked.
Built in 90 days. From brief to a working AI business application.
Machine manuals in the system. The full set of machine manuals was ingested, so the application understands error codes and operational routines for each asset, not only past tickets.
Retrieval augmented generation (RAG). In plain English: before the AI answers, it looks up the relevant past work orders, recorded fixes and manual sections, then answers from those sources. Within 3 months this lifted information-recall accuracy above 95%.
An automated 5 Whys workflow. Rather than leaving technicians to search freely under pressure, the application guides them through the 5 Whys method to the root cause, then records the fix so similar future work orders get the correct fix.
Five whys, asked in order.
- 01
Why did the fault appear?
Start from the symptom the technician sees on the floor.
- 02
Why did that happen?
Pull matching work orders and the relevant manual sections.
- 03
Why is that the case?
Narrow from the first cause to the underlying one.
- 04
Why was that missed?
Check procedures, intervals and previously recorded fixes.
- 05
Why did the process allow it?
Land on the root cause, confirm the fix and record it for next time.
Clear jobs on both sides.
The AI’s job
- Recall the right past work orders, fixes and manual sections
- Understand error codes and operational routines from the manuals
- Guide the 5 Whys root-cause workflow
- Structure each recorded fix so it can be found again
The technician’s job
- Own the diagnosis and the final call on the floor
- Confirm the root cause and apply the fix
- Record what actually worked
- Keep the knowledge honest for the next person
Put your own history to work.
If your business has expertise trapped in past jobs, manuals and people’s heads, this is the shape: ingest it, retrieve it at the moment of need, and give people a structured path to the right answer.
Expertise trapped in past jobs?
Let’s talk about the history in your business and whether an AI Engineering package is the right way to put it to work.
Or email carl@carldeantucker.com