Built in Malaysia On-premise AI Custom models

AI that runs on your premises, trained on your work.

Little AI Labs designs on-premise models, custom language models, and LLM automation workflows. Your data stays on infrastructure you control. We sit on top of the tools you already run.

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On-premPrivate models on your infrastructure
CustomFine-tunes and retrieval on your data
WorkflowsAutomation that writes into real systems
The problem

Your work should not have to
leave the building.

A public chatbot is a rented brain. Most of what a Malaysian organisation actually needs — private inference, a model that knows the house style, and a workflow that finishes the job — never ships as a chat window.

01

Data leaves, and you still own the risk

Prompts, contracts, tickets, and customer files sent to a public API leave the building. For many desks that handle client or operational data, that is already the wrong architecture.

02

Generic models give generic answers

Off-the-shelf models do not know your SOPs, your codes, or your Bahasa. Fine-tuning and retrieval against your own corpus is what makes the output usable on a real shift.

03

Chat does not close the loop

Drafting in a sidebar is not automation. The work is classify, route, write back, and hand off — into the ERP, the inbox, the ops sheet, the chat the team already uses.

04

Token bills scale with every query

Cloud APIs are right for spikes and experiments. Steady internal load, once it is real, is cheaper and more predictable on hardware you control — with no per-token meter from a US vendor.

What we do

Three services. That is the whole offer.

This is what “AI services” means here: private models on your infrastructure, models adapted to your domain, and workflows that use them. Not a hardware shop. Not a ChatGPT install. Not a multi-year transformation deck.

Primary

On-premise & private AI

Open-weight models deployed on your servers, a private cloud, or an air-gapped environment. Chat, search, and inference without sending prompts to a public API.

  • Model selection and serving (vLLM, Ollama, or your stack)
  • Private retrieval over your documents and systems
  • Access control, audit logs, encryption in transit and at rest
  • Air-gapped or default-deny outbound when policy requires it
Primary

Custom models

The base model is a starting point. We adapt it to your terminology, your documents, and the questions your team actually asks.

  • Fine-tuning (LoRA / QLoRA) on your corpus
  • RAG pipelines with evaluation on real prompts
  • Domain and Bahasa Malaysia where the work needs it
  • Quantisation and serving so it fits the hardware you have
Primary

LLM automation workflows

Agents and pipelines that classify, draft, route, and write into the tools you already run. One workflow first, measured, then the next.

  • Document intake, extraction, and routing
  • Internal copilots grounded in approved sources
  • Connectors into ERP, sheets, email, WhatsApp, APIs
  • Guardrails: what the model may do, and what stays human
How we work

One workflow, on your infrastructure.

Serious AI services shops do not start with a platform. They start with a bounded job, a data boundary, and a definition of done you can test.

01

Scope

Name the job, the system of record, the person who owns exceptions, and the data that must not leave. If it is not a first workflow, we say so.

02

Design

Model choice, where it runs, how it retrieves, which tools it may call. One document your technical and compliance people can sign off on.

03

Deploy

On machines you own, a private cloud you control, or an air-gapped box. We spec hardware when needed; we do not sell towers as the product.

04

Operate

Handover, runbooks, evaluation against your prompts. Optional ongoing support for model updates, monitoring, and the next workflow.

Public API chatbot Hardware reseller Little AI Labs
Where it runs Vendor cloud A box in your office Infrastructure you control
What you get A chat window GPUs and a runtime Models + retrieval + a working workflow
Your data Leaves as prompts Stays, if configured Stays by design
Knows your work Only if you paste it Not the product Fine-tuned and retrieved against your corpus
Writes into your tools Rarely No That is the point
Data & security

Collected lightly, managed carefully,
on your side of the wall

The questions a technical or compliance team asks before any engagement starts.

Where it runs

On-prem, private cloud, or air-gapped. We do not require a public model API. Hybrid is possible when the constraint is real but not absolute.

How we handle data

Encrypted in transit and at rest. Access scoped by role. We do not train a third-party model on your corpus, and we do not pool client data.

How we integrate

Read existing systems first. Write-back only where you approve it — a ticket, a sheet, a chat, an API. We do not rip out the system of record.

Who it belongs to

Weights, prompts, evaluations, and logs from your deployment stay yours. A mutual NDA is ready before any deeper scoping call.

Also: a vertical product

Airport Speed Mate, for Turnaround Operations Control

Secondary to the services work. It is how we prove we can ship operational AI into a real desk — not a workshop slide.

Turnaround Operations Control

The clock that desk still does not have

Some airline groups stood up Turnaround Operations Control so one room can own the 30–35 minute ground clock for the whole fleet. The desk still stitches an ops tracking tool, a Sheet, and Workvivo. Airport Speed Mate sits on top of those tools: live tracking, delay prediction, and operational remarks written from timestamps — not from memory.

What it watches

On-chocks through boarding: bags, fuel, IFC, cleaning, lav/water, engineering. Ranked by risk. The reason, not just the flag.

Where it lands

Workvivo, WhatsApp, and the sheet the shift already uses. We do not ask the desk to adopt a new chat app.

Who it is for

TOC first, then group engineering / MRO. Handlers later. Adjacent to OCC and technical ops — not a replacement for them.

TOC conversations continue. If you run that desk, write to us and say so. It is not the public headline of this site.

Engagement

Scoped after a real look at the work

We do not publish a menu of package prices. Discovery names the first workflow and the boundary. The build is quoted from that, in writing.

Start here

Conversation

A short call. The job, the data, the constraint. Enough to know whether we are the right lab — or to say we are not.

Then

Discovery

A bounded, paid look: data readiness, model path (API, RAG, fine-tune, or on-prem), compliance notes, and a written next step.

Then

Build & operate

One workflow in production on your infrastructure. Handover, then optional support for updates and the next job.

Typical first builds land in weeks, not a procurement year — provided the data boundary is clear and we can read the systems the work already lives in.

Abstract AI network representing technology built in Malaysia
🇲🇾 Built in Malaysia Local delivery On-prem & private cloud
Why Malaysia

Built where the data has to stay

Malaysian organisations are being asked to adopt AI and to keep personal data on shore. We design for that tension: local delivery, models that can run here, workflows that fit how teams actually work.

  • 🔒
    Data residency by designProcessing on infrastructure you can point to in Malaysia, when that is the requirement.
  • 🗂
    Location is an architecture choiceA public model does not take the duty off you. We treat where the data sits as a constraint in the design, not a footnote.
  • 💬
    Language that matches the deskEnglish and Bahasa Malaysia in the corpus, the prompts, and the evaluation — not as an afterthought.
  • 🛠
    Sit on what you already runSheets, ERPs, WhatsApp, internal search. The model comes to the tools. The tools do not get replaced.
Get in touch

Tell us the job and the constraint

Operations, IT, compliance, or a partner conversation — if the work has to stay on your side of the wall, write to us.

[email protected]

Public contact for projects, partnerships, research, and investor conversations.