AI understands language. We help it understand people.
Language is only part of what a person means. Attention, confidence, movement, effort and physiology carry the rest. As software moves from tools we operate to agents that act for us, the distance between inferred intent and actual intent stops producing a bad suggestion and starts producing the wrong action, taken on its own. We work on closing that distance.
Sense
Read the signal at the source: neuromuscular activity, movement, context. Not a proxy for it.
Interpret
Turn the signal into intent a machine can act on, with the uncertainty kept visible.
Apply
Put it into a working product and test whether it changes what a real person does.
Where we are looking now
Three explorations at the intersection of AI, human intent, trusted knowledge and sensing. Some begin as software. Some need hardware. Every one begins with a question that can be tested.
Human intent
How can AI systems better understand attention, confidence, movement, effort and readiness to act?
The company reads intent from the neuromuscular signals behind natural movement. We build software on that depth.
Trusted knowledge
How can AI accelerate expert work while keeping sources, uncertainty and human approval visible?
Expert judgment stays legible: what the system knows, how sure it is, and who signed off.
Physical AI
How can wearable and environmental sensing provide information that cameras or prompts cannot reliably capture?
Wrist-based surface EMG, movement and physiological signals add what vision alone can miss.
Proof of method: Mudra Experience Studio began as a studio test run. The signal was strong enough to earn its next stage. That is how we decide what to build.
From a signal to a working product
A small senior team sets direction and owns every decision. AI agents multiply execution across research, design, engineering and validation. Judgment stays human; capacity does not.
Sense
Start from real friction, not a technology looking for a use.
Prototype
Build the smallest working experience that can challenge the central assumption.
Validate
Test with the people who have the problem. Look for behavior, not applause.
Scale or stop
Develop what produces evidence. Change or close what does not.
The Innovation OS. Evidence before scale.
A written, repeatable way of moving from a real problem to a validated product. Every idea must earn the next step. Only four signals earn it: payment, repeated use, a strong reaction, or unprompted interest.
Listen
Collect signal from inside the company, from the market and from partners who bring us a problem. No solutions yet.
Frame
Write the problem in one fixed form, so any two problems can be judged side by side, whoever brought them.
Prototype
Build the smallest useful product that can challenge the riskiest assumption, and put it in front of real people.
Validate
Grade the evidence, not the pitch. Only four signals count: payment, repeated use, a strong reaction, unprompted interest.
Build
Scale what earns it. A documented stop is a result too, and it feeds the next round.
We bridge research signal to working product.
ai6labs is the innovation studio of Wearable Devices Ltd., backed by its experience in neural interfaces, surface EMG, consumer hardware and human-machine interaction.
People who have shipped products and launched businesses, working with an internal agentic team on a process that is written down. We do not present ideas. We present evidence.
Nasdaq: WLDSBring us a problem worth proving.
We work with companies, researchers and domain experts who have a meaningful problem, access to the people experiencing it, and the willingness to test a solution honestly.