We break things
on purpose.
The Lab is REKA's research engine: where we prototype the technology most companies only read about, and where tomorrow's products start as today's experiments. Running since 2016.
We build where the physical world runs out of tools.
Most technology defaults to the digital: software that runs on data, cloud services that retry on failure, models that live on a screen. The physical world follows different rules. Failure has real consequences. Latency kills. Privacy is not optional. Data does not always leave the building. Most research ignores this. We do not. Every frontier we pursue passes four filters.
Machines, sensors, infrastructure, spaces, bodies. If the problem only lives in software, someone else will solve it. REKA’s work runs on matter.
Digital automation assumes it can retry. Physical operations cannot. Most platforms were designed for a world where failure is reversible. We build for the world where it is not.
The science must be mature enough to attempt and the market early enough that our work compounds. We do not chase what is already crowded, and we do not start what the physics cannot yet support.
We need a minimum case where the question can be answered cleanly and in a reasonable time. If there is no path to a real proof, we do not start. We begin small and widen from ground we have proven.
Go to the root.
Not the symptom. Not the technology that happens to be trending. The actual structure of the problem, inside its domain, governed by its physics or its science. We go there first, before we name a tool, before we write a brief, before we run a model. That is not a process we follow. It is the only honest way to do research.
Hold the uncomfortable position.
The most defensible position is rarely the most popular one. We hold the position the evidence supports, and we say so out loud, even when it makes us harder to sell, less quotable in a press release, or less exciting to a prospect who wants to hear that their technology of choice is the answer.
We declined to call our self-driving work “Level 5 capable” when the industry was using that language to raise capital. We called it what it was: a bounded-domain system on a proven operating domain. We did the same when everyone reached for the largest frontier model. We did the same on quantum. contrarian disciplineTell the truth about the timeline.
What works today, what is two to five years out, and what is speculative are three different things that require three different responses. We build for the first, prepare for the second, and refuse to sell the third as imminent. The timeline shapes the experiment. The experiment does not bend to the timeline.
The Now / Near / Far tables on every frontier page were written before the belief sections above them. We established what the evidence actually supports, then wrote the position. Not the other way around. That order is not cosmetic. It is the discipline. honest horizonsResearch earns its place.
Every experiment has a defined exit condition: it becomes a product, it becomes a service, or it stops. We do not carry work indefinitely on the basis that it might eventually be useful. If it cannot close back to something a client can deploy, we document what it taught us and move on. Nothing here is theoretical for its own sake.
EXP-01 became Kinetik after proving bounded autonomy on Malaysian public roads. EXP-04 became PLExyz after proving edge-computed spatial tracking at operational accuracy. Every active experiment is a candidate, not a commitment. applied at the intersectionAll research begins with the four filters and is evaluated against these three principles
Each frontier passed the four filters. Each holds a position that is contrarian but defensible. Each has a time horizon we are honest about.
Autonomous Driving
We do not chase drive-anywhere. We engineer autonomy that runs and pays inside a known operating domain, on an open, auditable stack, on our own cross-platform hardware, with a human always one step away through teleoperation. Then we widen it.
A decade of field R&D from Malaysia’s first self-driving car on open roads in 2016 to autonomous mobile robots working indoors and outdoors today.
“Autonomy is a domain problem, not a model problem.”
AI & LLMs
Most teams reach for the biggest frontier model in someone else’s cloud and build backward from there. That is usually overkill, costly at scale, slower, and it ships your data offsite. We start from the constraints: privacy, latency, scale, the actual task.
The answer is usually a smaller, private, grounded system that runs where your data already lives. Sovereignty is the default, not an upgrade.
“Stop starting with the model.”
Quantum
Most “quantum wins” today are quantum-inspired classical methods that run on ordinary hardware right now. Real quantum advantage is narrow and years from routine. We do not sell the dream.
We frame hard problems the quantum way, solve them with what runs today, and build the formulation that will transfer cleanly to real quantum hardware when it earns its place.
“It is overhyped. That is exactly why to move now.”
Before we build anything.
Most technology work starts with a tool and looks for a problem to justify it. We start earlier than that. Every experiment in the Lab opens the same way, with five questions that have to be answered before anything is built or bought.
We map the domain first. What are the physical constraints? What does the relevant science say about what is possible and at what cost? The tool, the model, the platform: all of that comes after. If the physics does not support the claim, no amount of compute changes that.
Every experiment begins with a question that can be answered with evidence. Not “can we apply X to this space?” but “does method Y solve problem Z under constraints A, B and C?” If we cannot articulate what a failure looks like, we do not have an experiment. We have a hope.
Before we claim any improvement, we establish what the current state actually achieves. A result without a baseline is not a result. We measure against the real alternative, including doing nothing, not against an absence of comparison.
We find the tightest domain where the question can be answered cleanly, then we answer it there. Expanding scope before proof is how research becomes unfalsifiable. We prove the minimum case, document it honestly, then ask whether it is worth widening.
At a defined point in every experiment we ask whether the work can become something a client operates. If yes, it graduates. If no, it closes. The Lab is not a holding space for interesting ideas. It is a pipeline with a decision at the end.
What the Lab has built.
Every experiment the Lab has undertaken, with its current status. Two have earned their way into products. The others are active candidates or ongoing research. Each one opened with a falsifiable question.
Self-Driving Car
Proven. A bounded operating domain on a modular, auditable stack, running on our own edge hardware across vehicle types. The question was whether it was achievable without a bespoke platform. It was. → Kinetik
UVC Sterilisation
Active. The science is dose-response biology and robotic path planning for even coverage. The hard part is the safety interlock architecture: knowing when the system must stop regardless of task completion.
Cube Satellite
Research stage. The discipline is radiation-tolerant electronics and miniaturized sensor integration. Most of the early work is component selection and failure-mode analysis before anything goes near a launch manifest.
GPS & Asset Tracking
Proven. Dead-reckoning fused with GNSS and adaptive connectivity switching, inference running at the edge. The constraint that shaped it: environments where connectivity is intermittent and privacy is non-negotiable. → PLExyz
IoT Dev Board
Active. The work is PCB architecture for mixed-signal sensing, firmware design for sensor fusion, and power budget optimization for field conditions. On the third revision, driven by sensing use cases that keep emerging from PLExyz deployments.
Quantum × AI
Frontier. The method is reformulating known hard problems as QUBO or Ising models and solving with quantum-inspired classical solvers, proving value on our existing baselines. The hardware question comes after the formulation question. → Esoterik
A real R&D house, not a lab in name.
AI & Machine Learning
Perception, prediction, and decision models that hold up outside the demo.
Robotics & Autonomy
From open-road autonomous vehicles to remote-operated machines.
Sensing & IoT
Private-by-design sensing, edge computing, and spatial intelligence.
Microelectronics
Custom PCBs and hardware, from dev boards to satellite components.
Materials & Bio
Applied research like UVC sterilisation, where software meets the physical.
Quantum × AI
Frontier research into quantum-assisted optimisation and intelligence.
Experiments that earn their keep.
The Lab is not a cost centre; it is our pipeline. What proves itself in research becomes a product, so every client benefits from a decade of experiments. Two have already earned their place. The rest are candidates.
// Research that runs in the physical world
Got a hard research problem?
We partner with universities, institutions, and companies on applied R&D. Bring us the impossible-sounding one. We will tell you plainly what the evidence says about it.