AI & LLMs
The most important thing AI can do isn’t answer your questions. It’s know what’s standing in your way before you do.
Most AI is built to respond. We build it to understand your context and constraints, starting from privacy, latency, scale and the actual task rather than the biggest model. The answer is usually a smaller, private, grounded system that runs where your data already lives.
- Deployment
- On-prem, private cloud, edge or air-gapped
- Data protection
- Aligned with Malaysia’s PDPA and Vietnam’s PDPL
- Models
- Open-weight and commercial LLMs, vision, classical ML
- In production
- Evaluated, monitored, human in the loop
Stop starting with the model.
Constraints first
We start from what the system needs to do, not the model. Privacy, latency, scale, the actual task. Answer those honestly and the right architecture designs itself. It’s usually smaller, faster, cheaper, and more private than the default reflex.
Private by design
The AI that truly knows you cannot send your data away. Most AI vendors send it to someone else’s cloud; we can run models, including open-weight LLMs, inside your walls, on your data, under your control. That matters under Malaysia’s PDPA and Vietnam’s PDPL, and for finance, healthcare and government.
Grounded in your reality
Retrieval over your own sources beats a bigger model on bounded tasks. Answers come from what your people and data actually know, not from what a model memorised at training time. On a defined domain this wins on accuracy, cost, and speed simultaneously.
Proactive, not reactive
Systems that surface, alert, and act, not just respond. The endpoint isn’t a better search box. It’s intelligence that watches your situation and moves before you realise you needed it to. Janka, Tinara and Ripple automation, at AntaraX, are steps on that path.
Because we also build AI that runs on real vehicles and machines, we engineer for reliability: monitoring, evaluation and human-in-the-loop oversight, the difference between a demo and a system that works at 2am. Related reading: Your team can build AI. But can they make it work?
SETHThe answer is usually a smaller, private, grounded system that runs where your data already lives.
ReadoutInside your walls, on your data, under your control. Hover or tap a part to read what it does.
Pick your constraints.
Not every problem needs a frontier model. Tell us what actually constrains you and watch the sensible architecture fall out. A guide, not gospel: real recommendations come after we see your data and traffic.
Public data, low volume, no latency pressure? Do not over-build. Start on a hosted frontier API, prove the use case, then optimise cost and privacy later.
Cloud frontier: fine here.
A full AI platform, not a single model.
A model is the easy part. Putting one to work safely, on your data, in production, takes a stack. We assemble these layers on infrastructure you choose, your servers, your private cloud, or the edge, so you get the upside of modern AI without handing your data to someone else.
SETHHuman review sits across the loop wherever a wrong answer is costly.
ReadoutHover or tap a layer to read what it does.
The layers as a table
| Layer | Name | What it does | Typical |
|---|---|---|---|
| L1 | Governance & oversight | Evaluation, guardrails, drift detection, audit logs, human-in-the-loop. | eval · guardrails · HITL · audit |
| L2 | Applications | Assistants, copilots, autonomous agents, search and dashboards. | agents · copilots · APIs |
| L3 | Orchestration & RAG | Retrieval, tool-use and agent workflows grounded in your knowledge. | RAG · vector DB · tools |
| L4 | Models | LLMs, computer vision and forecasting, fine-tuned to your domain. | LLM · CV · LoRA / PEFT |
| L5 | Serving & inference | Optimised inference on GPU, on-prem or at the edge. | vLLM · Triton · quantisation |
| L6 | Data | Ingestion, labelling, embeddings and feature pipelines. | ETL · embeddings · labelling |
| L7 | Infrastructure | On-prem, private cloud or edge, secured and access-controlled. | on-prem · private cloud · edge |
From data to dependable AI.
We don’t stop at a demo. Every model runs a loop, so it stays accurate after launch, not just on launch day.
| Prepare | Ingest, clean, label and embed your data. |
| Fine-tune | Adapt models to your domain with LoRA/PEFT. |
| Measure | Accuracy, safety and bias against real cases. |
| Serve | Optimised inference, on-prem or edge. |
| Watch | Drift, cost and quality, with guardrails. |
| Iterate | Feedback loops retrain and sharpen it. |
What this research becomes.
This work lives in REKA’s Heuristik division. When it proves repeatable, it may become a product at AntaraX, the product company created from REKA research. Three of them sit at different points on the path from grounded to proactive intelligence.
| Product | AntaraX family | What it does |
|---|---|---|
| Janka Available | Tide | AI inventory forecasting. Reads real consumption, recommends when and how much to reorder, and drafts the order for a person to approve. |
| Tinara Pilot | Tide | Malaysia-first B2B intelligence. Ranks companies by fit, signals and match, and writes a brief: who to call and how to open. |
| Loom Pilot | Loom | Data from many sources woven into one picture, and synthetic data where real data is short or too sensitive to use. |
Tinara and Loom are AntaraX closed pilots. Janka is available now at antarax.ai.
Private grounded systems
Smaller models, grounded in your data, on-prem or private cloud, paying today in cost, speed, and control.
Grounded agents
Domain-tuned private models taking multi-step actions inside your systems, monitored and auditable. AntaraX Ripple watches every step.
The model commoditises
When raw capability is everywhere, the durable edge is your proprietary data, grounding, and sovereignty. We are building for that now.
Specifications.
Exact models, frameworks and infrastructure are chosen per use case and data-residency needs.
- Model types
- Open-weight & commercial LLMs · vision models · classical ML / forecasting
- Adaptation
- Prompt engineering · RAG · fine-tuning (LoRA / QLoRA / PEFT) · distillation
- Serving & inference
- vLLM · Triton · GPU acceleration · quantisation (INT8 / 4-bit) for edge
- Retrieval (RAG)
- Embeddings · vector database · hybrid search · grounded citations
- Computer vision
- Detection · segmentation · OCR · tracking · edge inference
- Deployment
- On-premises · private cloud · edge · air-gapped options
- Reliability
- Offline + online eval · monitoring · drift detection · human-in-the-loop · rollback
- Governance
- Access control · audit logging · PDPA (MY) & PDPL (VN) aligned