Who lands
Turnaround coordinator, TOC lead, OCC / network ops supervisor, airline IT asked to put models on ops data without a public API.
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 workflows for airline and ground operations, healthtech and hospital-ops data, and logistics turns. Your data stays on infrastructure you control. We sit on top of the tools you already run.
On-premPrivate models on your infrastructure
CustomFine-tunes and retrieval on your data
WorkflowsAutomation that writes into real systems
Industries
Four desks. Same architecture.
We do not sell a public chatbot. We put private models and one workflow on the systems these operations already run.
Airlines
Little AI Pilot for the turnaround coordinator — TOC, OCC, the clock.
Airline ops and ITHealth
Drug pricing, claims integrity, outpatient ops data that cannot leave.
Healthtech / payor / hospital-ops ITLogistics
Terminal and freight turns — timestamps to remarks — plus the documents around them.
Terminal, 3PL, and warehouse opsGround operations
Little AI Pilot for the station clock — ramp, bags, handlers.
Station leads and GHAsAirlines
Private AI for the turnaround coordinator
Movement data, delay codes, and station remarks do not belong in a public model. Little AI Pilot sits on the tools TOC already runs. Ground operations is the same product from the station side.
Who buys
Airline ops leadership + IT. Compliance is in the room because movement and delay data is sensitive even when it is not passenger PII.
Turnaround Operations Control
Little AI Pilot — 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. Little AI Pilot 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
The turnaround coordinator first (TOC). Then group engineering / MRO. Ground operations is the station door. Adjacent to OCC; not a replacement for them.
Named work. TOC conversations continue. It is not the public headline of this site.
Ground operations
Little AI Pilot for the station clock
Airline operations includes ground operations. The coordinator’s clock is bags, fuel, IFC, cleaning, engineering — on the ramp as much as in the TOC room. Little AI Pilot sits on the handler’s sheet and chat.
Who lands
Station manager, ramp lead, handler shift running bags/fuel/cleaning/IFC on the 30–35 minute clock, GHA IT.
Who buys
Ground company ops + IT, or the airline’s ground-ops contract owner buying station-side access to the same clock.
Turnaround — station / ground
The same clock, on the handler’s tools
Little AI Pilot watches the turn from on-chocks through boarding — bags, fuel, IFC, cleaning, lav/water, engineering — and writes operational remarks from timestamps into the sheet and WhatsApp the station already uses. Airline-group TOC is the other door, above.
What it watches
The ramp sequence: bags, fuel, IFC, cleaning, lav/water, engineering. Ranked by risk. The reason, not just the flag.
Where it lands
The handler sheet and WhatsApp the station already uses. We do not ask the ramp to adopt a new chat app.
Who it is for
The station shift first. Airline TOC is the other door. Adjacent to OCC; not a replacement for them.
Health
On-prem AI for drug pricing, claims integrity, and outpatient ops
A public chatbot is the wrong architecture for claims, e-prescriptions, and formulary files. We put open-weight models on infrastructure the healthtech or hospital group controls, and write into the auditor queue, sheet, or API the desk already uses.
Who lands
Healthtech / digital-MCO / TPA product or IT, payor claims/formulary lead, clinic-network or pharmacy-ops IT, a hospital-group digital lead whose claims and e-Rx data cannot leave Malaysia.
Who buys
Healthtech or payor IT + compliance, with an ops sponsor on pricing, claims, or outpatient routing. Not an EHR replacement, not a diagnostic device, not a patient-facing chatbot.
Example scope
Private retrieval / data platform
Over approved claims, e-Rx, and formulary sources on infrastructure the healthtech or hospital group controls.
Example scope
Claims / FWA copilot
Duplicate/phantom flags and substitution notes into an auditor queue the payor already uses — human in the loop, not a denial bot.
Example scope
Clinic / ops documents
Itemised billing and approved-source copilots that write into the sheet, inbox, or API already in use.
These are first workflows we would scope. They are not case studies. Do not send patient or claims files to hello@. Discovery is a call, under NDA. We do not offer a diagnostic product, a medical device, or a patient-facing chatbot. PDPA and data residency are constraints we design for — not a certification claim.
Logistics
Private models on the turn — berth, terminal, freight clock
The work is the cycle time you already stamp, plus the documents around it. We run open-weight models on infrastructure you control: delay remarks from timestamps, and extraction/exceptions into the TMS, WMS, or sheet you already run.
Who lands
Terminal / berth / gate ops, freight forwarding supervisor, 3PL ops, warehouse lead, logistics IT.
Who buys
Ops + IT. Commercial and movement data leaving to a US API is the compliance objection.
Example scope
Terminal / berth / truck-cycle clock
Timestamps to delay/slip prediction and operational remarks into the ops sheet or chat the shift already uses. Same clock engine as aviation; different yard.
Example scope
Exception classify
Delay, damage, short-ship, hold — into the WhatsApp or email the shift already uses.
Example scope
Document extraction
Customs / commercial invoice / packing-list / BL into the TMS or sheet — secondary to the turn, not the only story.
These are first workflows we would scope. They are not case studies. No named terminals. We do not replace the TOS/WMS/TMS.
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.
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.
🇲🇾 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.
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Data residency by designProcessing on infrastructure you can point to in Malaysia, when that is the requirement.
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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.
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Language that matches the deskEnglish and Bahasa Malaysia in the corpus, the prompts, and the evaluation — not as an afterthought.
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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
Airlines, health, logistics, or ground ops — say which desk. 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.