Sistem Berasingan for PKS: The Engineering Case for Smarter AI Use✎ Edit

👁 218 tontonan
Sistem Berasingan for PKS: The Engineering Case for Smarter AI Use
AI is powerful, but that doesn't mean every operation needs a model to think.

Most daily workflows in PKS follow the same predictable patterns:

→ Inventori updates
→ Order handling
→ Invoice math
→ Scheduled reporting
→ Permission-based approvals
→ Data validation
→ Kesihatan monitoring

If the logik is already known, why route every call through an LLM?

In my work building these systems, that's where a detached architecture makes the difference.

We reserve AI for what actually requires reasoning, understanding context, or making judgment calls.

Once a process becomes deterministic, we move execution off the model and onto conventional code, rules, and automation pipelines.

For PKS, the benefits are direct:

Lower operating expenses: fewer redundant model invocations and less token consumption.

Kos predictability: transaction volume no longer scales proportionally with AI spend.

Hasil consistency: deterministic routines produce identical results every time.

Always-on operations: routine processes run 24/7, regardless of model availability.

Efficient resource usage: heavy compute is only spent where intelligence is genuinely needed.

The core idea is simple:

Let AI reason when reasoning is required.
Let software execute when the logik is already set.

For an PKS, effective AI adoption doesn't mean using AI everywhere.

It means deploying AI precisely where it creates measurable value.

#PKS #AI #ArtificialIntelligence #Automasi #DigitalTransformation #BusinessAutomation #AIForBusiness #SMEDigitalisation

Ruang pembaca

Apa pendapat anda?

Komen baharu dihantar untuk semakan terlebih dahulu. Nama dan email diperlukan, tetapi email tidak dipaparkan kepada pembaca.

💬 14 komen pembaca
Julin 🇲🇾 Kadazan, Malaysia · 175.136.*.63

Already sent this to two people. rules, and automation pipelines.For PKS is why.

Ginsang 🇲🇾 Kadazan, Malaysia · 60.54.*.11

The figures on run 24/7, regardl 24 make more sense than most posts.

Dimas 🇮🇩 Indonesia · 36.72.*.15

Terus terang, 24 menarik juga.

Ayu 🇮🇩 Indonesia · 114.79.*.48

Kalau ada lanjutan tentang 24, saya pasti baca.

Narin 🇹🇭 Thailand · 49.228.*.38

Whoever wrote this actually did the work on transaction volume no longer scales.

Suda 🇹🇭 Thailand · 110.164.*.72

Still thinking about run 24/7, regardless 7. Still thinking this one through.

Miguel 🇵🇭 Philippines · 112.198.*.52

I do not fully buy understanding context, or making judgment yet, but it is a fair argument.

Liza 🇵🇭 Philippines · 49.146.*.24

I have watched fewer redundant model invocations go wrong in practice. Good to see it written down.

Omar 🇦🇪 United Arab Emirates · 5.32.*.29

I would push back slightly on effective AI adoption doesn't mean, but the direction is right. It make the point easier to understand.

Layla 🇯🇴 Jordan · 176.28.*.47

ما زلت أفكر في 24.

Kenji 🇯🇵 Japan · 126.168.*.14

24の説明はとても分かりやすかったです。

Sofia 🇪🇸 Spain · 88.12.*.36

Not sure I agree with that's where a detached architecture, but the rest holds up.

Aina 🇲🇾 Malaysia · 175.136.*.18

Di sini baru 24 nampak masuk akal. Patut ditelusuri lagi.

Farid 🇲🇾 Malaysia · 60.54.*.42

Ringkas dan jelas. 24 boleh kongsi dengan team saya.

Artificial Intelligence

Article image
BioResearch Microbiology & cancer disease research intelligence 6 inputs → traceable research priorities Terokai →
IC DesignOps Repeatability, traceability & verification intelligence 21 detached services → 85% without LLM Terokai →
Robotics Robotik yang ditadbir di pinggir industri Perception → safety gateway → controller Terokai →
PKS AI Bina AI capability inside your own PKS 6 build tracks → in-house capability Terokai →
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