Hylos Labs

Agent‑native runtime for the physical world.

Frontier models can reason about the physical world. Hylos builds the infrastructure they need to act in it: a persistent 3D scene with addressable objects, shared controls, and verification before changes commit.

The scene carries context across turns, tools, and models.

Persistent spatial stateShared human & agent controlsModel-agnostic

The gap

Generation was the wrong half.

World models can produce a convincing room. Frontier models can call Fusion, Omniverse, and Isaac. What comes back can look finished and still be physically false: objects that interpenetrate, contact that never happens, a collision shape standing in for the part. Success on that output does not mean the thing can be made. It does not mean the real arrangement was tested.

  1. It looks finished.The new part is in the drawing. The shell around it is still the old one. Nothing has asked whether they still fit.
  2. Only the part changed.The part gets larger. The lid, the mounts, and the opening stay drawn for what used to be there. The file shows a finished change. The assembly was not redone.
  3. It doesn’t close.Put it together and the lid doesn’t meet the body. That is the first check against the real arrangement. Before that, you had a picture you could not manufacture and could not trust as a test.

Check whether it still fits before the change is committed. That is the scene the next action inherits.

The file

Part in the file

Fit not checked

Part larger

Lid and mounts unchanged

Doesn’t close

The runtime

A compiler from intent to physical space.

Hylos turns intent and evidence into a persistent, typed 3D scene: objects the agent can address, relationships it can act on, and constraints that remain in force.

People and agents share its controls. Connected tools supply the measurements. The runtime holds the state each next action inherits.

  1. CompileTurn language, references, and geometry into objects, relationships, and declared requirements.
  2. ActPeople and agents propose changes through the same controls, against the current scene.
  3. VerifyMeasure the result in the connected backend. Check the affected requirements before accepting the change.
  4. CommitA pass updates the scene and its dependents. A failure returns as repair work or an explicit capability gap.
IMPULSE

The first thing we built on it is an engineer.

Of all the spaces an agent could operate in, a product assembly is the one where being wrong is cheapest to detect and most expensive to ship. A CAD kernel can measure whether the scene is true. That made product engineering the right place to prove the runtime, and the first thing worth selling on it. The result is Impulse.

You give Impulse the brief. It plans the parts, builds them in real CAD, measures whether they fit, move, and can be manufactured, and hands back only what passed. Engineering work you can hand off, because it comes back checked.

impulse.build live

Context in

Give it the brief. Change the brief. It keeps both.

Text, images, sketches, existing CAD. Add them up front or alter them once the work has started.

impulse.build live

Its engineering, your decision

It does the mundane work. It brings you the decision.

Fits, clearances, and construction stay in the loop. A real design choice comes back to you, with the plan attached.

impulse.build live

The build, in the open

Watch it engineer. Stop it whenever you want.

Part by part, the reasoning stays on screen. What lands is a verified build, every part credited.

impulse.build live

Stay in the loop

Ask a physical question. Change the design. It goes back in.

Does the lid open under its own weight? Impulse answers against the live product, and a change re-enters the same loop.

Use Cases

One spatial runtime. Different kinds of work.

A product assembly, a training environment, a workcell, or a building: each needs addressable objects, allowed actions, persistent constraints, and a way to verify what changed.

Impulse · current product

Product engineering

“Fit a larger battery into this enclosure.”

Keep mounting interfaces, clearance limits, and lid motion as scene requirements. Propagate the battery change through affected dependencies and check the revised assembly.

More of the engineering work can be delegated.

LAYOUT ALAYOUT BLAYOUT C

Robot learning · roadmap

Training & evaluation

“Run this task in 200 different kitchens.”

Define interactive objects, joint limits, and success conditions. Vary layouts and assets while preserving the task requirements used to evaluate each run.

Less custom setup for each environment.

Workcells · roadmap

Factory planning

“Replace this robot with a smaller one.”

Recompute reach, clearances, and dependent placements for the new robot. Carry constraints and the reasons for layout choices into each revision.

Faster answers before a physical reconfiguration.

Facilities & twins · roadmap

Installation planning

“Can we get this machine into the building?”

Turn site evidence into an operable scene of openings, routes, equipment, and access requirements. Revise the installation plan when the observed site changes.

Discover infeasible plans before installation day.

Illustrative use cases. Impulse is live; the environment SDK, simulation adapters, and workcell and twin applications are proposed. Building in one of these areas? →

Research and posture

We don't try to make the model trustworthy. We make the system trustworthy.

A model is allowed to be brilliant and wrong. It proposes; it never grades its own work. Every check runs on instruments the model cannot influence, and a change only becomes real after they pass. Better models will fail fewer checks. They will not get to skip them.

Papers & filings

In preparation

  • Falsification-Governed Language-Model Authoring Against a B-Rep Kernel

    coming soon
  • A Longitudinal Defect Taxonomy of Kernel-Verified LLM Geometry Synthesis

    coming soon
  • Counterexample-Guided Synthesis of Physical Assemblies with Intent-Preserving Repair

    coming soon
  • Verifier Signal Placement in Agentic Generation: Feed-Forward Constraints versus Rejection

    coming soon
  • Provenance-Anchored Measurement in Self-Correcting Geometry Pipelines

    coming soon
  • The Agent Runtime as a Checkable Program: Totality, Branded Invariants, and Adversarial State Walks

    coming soon

Company

Built by people who ship physical products.

Hylos Labs is a Canadian company. Impulse is the first product. We are talking with investors, design partners, and teams working at the edge of autonomy, simulation, and spatial AI.

Chris Da Silva

Founder · CTO

Founder of Hylos Labs and creator of Impulse. Built the spatial reasoning, CAD authoring, and verification architecture. Repeat technical founder, focused on making engineering work reliable enough to delegate rather than assist.

Randy Yang

Co-founder · Head of Product

Eighteen years bringing physical products from concept through commercialization. Runs ORYN Studio. Has launched award-winning products across mobility, child safety, healthcare, and consumer. Owns how Impulse fits a real product-development workflow.

Get in touch

Investors, partners, and design partners. Tell us who you are and what you are working on.