The A.L.EX method · knowledge that stays
When Alex leaves, the knowledge leaves too.
The A.L.EX method makes sure it stays.
Three ways for hands-on knowledge to enter the system. One thing always holds: it ends up in a validated knowledge base that anyone can call up.
① Knowledge comes in
② Extract and validate
③ Stays available
Passive
The system takes over
Almost no effort. Forms at the machine, voice input, ticket analysis.
Active
Active work
5 to 10 minutes a week. AI questions, buddy system, problem of the week.
Deep
Knowledge culture
1 to 3 hours. CDM interview, video walkthrough, diagnostic tree.
CORE
- ›Scan
- ›All data available
- ›AI chat helps
CONNECT
- ›Interaction by voice
- ›Structured work orders
- ›Knowledge is captured
COMPANION
- ›Extraction
- ›Diagnosis and processing
- ›Learning paths
- ›Decision support
① Knowledge comes in
Passive
The system takes over
Almost no effort. Forms at the machine, voice input, ticket analysis.
Active
Active work
5 to 10 minutes a week. AI questions, buddy system, problem of the week.
Deep
Knowledge culture
1 to 3 hours. CDM interview, video walkthrough, diagnostic tree.
② Extract and validate
③ Stays available
CORE
- ›Scan
- ›All data available
- ›AI chat helps
CONNECT
- ›Interaction by voice
- ›Structured work orders
- ›Knowledge is captured
COMPANION
- ›Extraction
- ›Diagnosis and processing
- ›Learning paths
- ›Decision support
01
Smoothly in
Knowledge arises as a by-product of the work. At the machine and by voice, not at a desk.
02
Checked, not guessed
The validation loop filters out workarounds before the AI scales them.
03
Stays in the business
Available at the machine, in the app and through the AI. Even when Alex leaves.
Extract → Validate → Scale
First extract, then validate, then scale.
The knowledge journey
From the head into the platform.
The graphic above shows how knowledge comes in and goes back out. What sits behind it, we show here in excerpts.
Scientific foundation
Tacit Knowledge (Polanyi)
Masters know more than they can say.
SECI model (Nonaka/Takeuchi)
Tacit knowledge only becomes valuable once it is shared.
Recognition-Primed Decision (Klein)
Experienced technicians decide before they think it through.
Extraction without validation scales errors.
Between capturing and using sits a step that many wiki and documentation tools simply skip. With A.L.EX that is exactly where the real work happens.
The validation loop
Knowledge that is no longer checked goes out of date. Even if it was captured well once. A.L.EX closes this loop instead of leaving it open after extraction.
Knowledge maturity
intuAid positions itself at the transition from validated to scalable. Most businesses today stand just before it.
How an experienced technician thinks before deciding, made visible.
The diagnostic tree sits behind the CDM interview from the graphic above. What it shows in detail, we discuss in conversation.
The question is not whether your knowledge leaves. The question is whether it stays.
Next step
We support you through the change.
From the first analysis to ongoing knowledge upkeep. Talk to us about your business.