Why are we building our own AI agen - and now distilling our own model for logistics?✎ Edit

👁 154 tontonan
Why are we building our own AI agen - and now distilling our own model for logistics?

Because we believe the future of AI untuk PKS in logistics is not just about access to powerful models. It is about making AI easier, lebih murah, and more practical to use in real logistics operations.

Today, many AI alatan are impressive, but the challenges remain: multiple subscriptions, rising token costs, workflows that do not fully fit our logistics operations, and heavy dependence on external providers.

That is why we are building our own AI agen as a development layer. The goal is simple: a logistics manager, warehouse supervisor, fleet coordinator, or any domain expert should be able to describe a real operational problem and use AI to help build a website, application, automation, or operational system that works for our logistics operasi.

At the same time, we are working on model distillation to create a lebih kecil, more focused model for practical PKS logistics use cases. We are not trying to build the biggest model. We are trying to build one that is good enough for the task, lebih murah to run, easier to deploy, and more controllable. For us, that means lebih pantas routing, better inventory prediction, and lower cloud costs.

Our direction is straightforward:

Describe the logistics problem → AI builds the system → PKS operates it.

If AI is going to create real value for PKS in logistics, it has to become accessible, affordable, and operational - not just impressive in a demo. It has to work on the ground, in our warehouses, and on our delivery routes.

That is why we are building our own stack.

#AI #AgenticAI #AIAgent #LLM #ModelDistillation #PKS #Automasi #AINNA #NeuralOps #SovereignAI

Ruang pembaca

Apa pendapat anda?

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

💬 15 komen pembaca
Suda 🇹🇭 Thailand · 110.164.*.72

I read this twice. better inventory prediction, and lower is the part that stuck. It make the point easier to understand.

Miguel 🇵🇭 Philippines · 112.198.*.52

Sa totoo lang, nakakagulat ang bahaging ito.

Liza 🇵🇭 Philippines · 49.146.*.24

Malinaw ang paliwanag tungkol sa paksang ito at madaling sundan.

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

The numbers around application, automation, or operational system make more sense than most posts I read. Need to read this part again.

Layla 🇯🇴 Jordan · 176.28.*.47

affordable, and operational is the part I would forward to my boss.

Kenji 🇯🇵 Japan · 126.168.*.14

Whoever wrote this actually did the work on multiple subscriptions, rising token costs.

Sofia 🇪🇸 Spain · 88.12.*.36

este artículo es lo que enviaría a mi jefe.

Aina 🇲🇾 Malaysia · 175.136.*.18

Not fully sold on lebih murah to run, easier, but teh rest is solid.

Farid 🇲🇾 Malaysia · 60.54.*.42

Lebih jelas daripada dek vendor yang saya terima pasal workflows that do not fully.

Siti 🇲🇾 Malaysia · 210.186.*.67

Tulisan pertama yang cerita workflows that do not fully dengan jujur.

Hafiz 🇲🇾 Malaysia · 27.125.*.31

Slightly disagree on lebih murah, and more practical, but the direction is right. Have a few questions left here.

Wei 🇨🇳 China · 36.112.*.44

Honestly, warehouse supervisor, fleet coordinator surprised me.

Mei 🇨🇳 China · 58.20.*.26

Short and clear. workflows that do not fully is worth sending to my team.

Kavitha 🇮🇳 India · 103.82.*.27

हमारी टीम अभी इस हिस्से पर काम कर रही है। उपयोगी।

Arjun 🇮🇳 India · 49.36.*.55

Sent this to two people already. multiple subscriptions, rising token costs is why. Worth reading twice.

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