VXN
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// AUTONOMOUS ASSEMBLY INTELLIGENCE

Robots that move
like the surface
is their world.

One unit. Any surface. Any task. Learns while it works.

Surface-native swarm units that traverse real geometry, understand the work, and execute without a script. The environment is the input. The task is the output. What operates today is more capable than what arrived.

INPUT
CAD / STL
BLUEPRINT → WORK PLAN
UNIT INTERFACE
CONFORMABLE
ANY SURFACE · ANY ANGLE
MVP SCOPE
FASTENERS
SCREWS · BOLTS · NUTS
INTELLIGENCE
ADAPTIVE
TRAINED ON REAL GEOMETRY
Custom PCB Design
3D Fabrication
Conformable Unit Architecture
Brushless DC & Servo Drive
GPU-Native Planning
Continuous Field Learning
Calgary, AB
// THE PROBLEM

Existing robots execute scripts.
They don’t understand work.

SURFACE
Current robots navigate a projection of the real world.

Every major autonomous system flattens 3D reality onto a 2D floor plan. Walls, curves, inverted surfaces — unreachable by design. The real world has never been a flat floor.

TASK
They execute scripts. They don’t understand what the work is.

A robot that tightens a bolt cannot weld a seam unless someone rebuilt it for that. The knowledge does not transfer. It has to be reprogrammed from scratch.

ENVIRONMENT
The model exists. The robot can’t read it.

Every environment worth automating already has a 3D model. The robot cannot use it. Months of integration before a unit touches the real world.

// TWO MOMENTS FROM THE SAME SYSTEM
INTERVENTION
A bolt is loosening on a running arm. The line does not stop.

The unit reaches the joint while the arm completes its cycle. Drives the bolt home. The arm keeps moving. Nothing stops.

ASSESSMENT
Wear identified. Data returned. Correction authorised.

A crack is forming on a running machine. The unit maps it, returns the data. A human authorises the fix. The same unit closes it. Machine never offline. Human never in a hazardous space.

ANY →
SURFACE · TASK
ENVIRONMENT
N+1
UNITS COMPENSATE
IF ONE FAILS
ZERO
CUSTOM INTEGRATION
PER ENVIRONMENT
01 · GEOMETRY
It reads the world before it touches it.

The actual structure — not a map, not a waypoint set. Known before a unit moves. The environment does not constrain the work.

02 · ADAPTATION
The task changes. The unit does not need to know in advance.

What a unit does in one context is not fixed to that context. A unit goes offline — the role redistributes. The work continues.

03 · COMPOUNDING
Time on-site is not cost. It is capability.

Not from an update. From operating in your specific geometry, under your specific conditions. The longer it runs, the sharper it gets.

SYSTEM WHY VOXARN OUTPERFORMS THE FIELD

What every other system gets wrong.

Built for a task, in one environment. Change either — rebuild. VOXARN is built differently. The capability transfers.

Current generationVOXARN
SURFACE RANGEFloor-plane only. Ramps at reduced confidence. Walls, ceilings, and curved surfaces are not navigable.Any surface at any angle — walls, curves, inverted surfaces. Full geometric fidelity from the actual structure.
UNIT INTERFACEFixed hardware geometry. Each unit is designed for one surface type. Multi-surface deployment requires multiple hardware configurations.Conformable architecture. Brushless DC drive and servo-actuated contact surfaces adapt to whatever geometry the unit encounters.
UNIT BEHAVIOURHomogeneous fleet. Every unit runs identical logic. One unit fails — nothing compensates.Specialized roles, dynamic coordination. Units divide labour and compensate for failure in real time without manual intervention.
ENVIRONMENT INPUTSensor-mapped during deployment. Weeks of integration before a unit touches the real environment.Direct from your existing engineering models. The structure your team already has becomes the traversal map.
FIELD IMPROVEMENTStatic from deployment. Performance on day one equals performance on day three hundred.Continuous improvement through operation. Field time builds capability specific to your environment.
FLEET SCALABILITYFixed architecture. Adding or replacing a unit typically requires downtime and system reconfiguration.Hot-swap capable. Add a unit — it joins immediately. Replace one — the fleet adapts. No reconfiguration. No downtime.
CAPABILITIES FIVE CORE CAPABILITIES
CAP-01 · GEOMETRY
True-scale surfaces, known before deployment.

The actual structure of your environment becomes the traversal surface. No translation step. No pre-deployment integration. If the model exists, VOXARN can operate in it.

SURFACE-NATIVE
CAP-02 · INTERFACE
One unit. Any surface. Any task.

The unit that traverses a pipe is the same unit that traverses a wall. What it does when it arrives depends on the work — not on which configuration was shipped.

TASK-AGNOSTIC
CAP-03 · COMPOUNDING
Field time is not cost. It is capability.

Every cycle compounds. Not from an update — from operating in your specific environment. The twelve-month system is not the day-one system.

CONTINUOUS IMPROVEMENT
CAP-04 · COORDINATION
Division of labour at swarm scale.

One stabilizes. One executes. One assesses while another routes. The swarm divides the work and compensates when a unit goes offline.

ROLE-SPECIALIZED
CAP-05 · CONTINUITY
Add a unit. Remove a unit. The fleet does not notice.

A unit goes offline — the fleet compensates immediately. A unit is added — it integrates. Downtime is not assumed. It is eliminated.

FAULT-TOLERANT
FIELD SPECS TECHNICAL ARCHITECTURE

The foundation that makes everything else possible.

VOXARN · multi-unit surface traversal · live simulation
UNITS 8
MESH_FACES 24,816
PLAN_MS 11
FRAME 0
SYS-ATrue Surface Traversal

Units navigate real surface geometry — no projection, no approximation. Walls, curves, and inverted surfaces are not edge cases. They are standard terrain.

SYS-BCAD-Native Environment Ingestion

Existing 3D models become the operational map. No hand-crafted waypoints. No sensor calibration pass.

SYS-CGPU-Native Unit Planning

Path planning runs entirely on GPU. CUDA and ROCm/HIP — NVIDIA and AMD. Scales to fleet size without a sequential bottleneck.

SYS-DProprietary Stack — Full Ownership

No external navigation dependencies. No third-party planning library. Every critical layer is owned.

ACTIVE STACK
Python · Taichi (GPU sim)
PyTorch (network training)
CuPy · NumPy (array compute)
CUDA (kernel dispatch)
ROCm / HIP (AMD GPU parity)
C++ (systems layer)
Vulkan 1.3 (render pipeline)
Custom PCB (in development)
DEVELOPMENT MILESTONES
Multi-unit GPU sim on arbitrary mesh
Surface traversal fundamentals
Physics constraints + energy modelling
CAD / STL environment ingestion
90% simulation fidelity threshold
PCB design — unit v0.1
Motor & servo control loop integration
Hardware validation — first unit
// SIM NOTE
Canvas 2D demonstration only. Production target: GPU compute kernels via CUDA and Vulkan. Set SITE_CONFIG.CAD_DEMO_READY = true to unlock the CAD demo tab when footage is ready.
MEDIA SIMULATION CAPTURES
SIM CANVAS · LIVE CAPTURE  
SIM STILL · AWAITING CAPTURE
CANVAS SNAPSHOT
HERO CANVAS · LIVE CAPTURE  
HERO CANVAS · AWAITING CAPTURE
CANVAS SNAPSHOT
// Live PNG snapshots from the running canvas renderer. Replace with production renders before launch.
SCREENSHOT · 001
SS-001
ASSET PENDING
SCREENSHOT · 002
SS-002
ASSET PENDING
SCREENSHOT · 003
SS-003
ASSET PENDING
// CAD DEMO footage placeholder — set SITE_CONFIG.CAD_DEMO_READY = true and drop footage at voxarn.com/demo/ to populate.
COMPANY ORIGINATOR
SUBJECT-ID: VXN-F01
Dev Bhatt
FOUNDER · VOXARN
VERIFIED
CALGARY, AB
CANADA
PRIOR VENTURE
Founder of ShapeSynth — a GPU-native quad retopology engine producing production meshes from raw 3D scans in 269ms end-to-end. Six-stage fully-GPU pipeline with novel sparse data structures for GPU cache hierarchy. Built on CUDA and inline PTX.
OPEN-SOURCE WORK
Builder of Axylith — a native research environment in C++20 with Vulkan 1.3 MTSDF rendering and full CI across GCC × Clang × sanitiser builds. Portfolio at devbhatt.dev runs a hand-written C++ mesh viewer compiled to WebAssembly with LIC field rendering and curvature heatmaps.
RECOGNITION
JMH Pitch Competition — $15,000 Deep Tech Award. Won with ShapeSynth, the prior GPU-native retopology venture. Selected from the Mount Royal University startup cohort in the deep tech category.
EDUCATION
CS + Data Science — dual major. Mount Royal University, Calgary.
DOMAIN EXPERTISE
Active engineer in mine and plant automation — the exact operational context where surface-native autonomy is most constrained by current technology. Employer: . VOXARN is an independent venture with no institutional backing.
FULL STACK
Python · Taichi · PyTorch · CuPy · NumPy · CUDA · ROCm / HIP · C++ · Vulkan 1.3 · GLSL · Emscripten / WASM · OpenGL ES3 · CAN Bus · Cloudflare · PCB Design (in development)
DEPLOY PARTNER INQUIRY

We’re looking for
the right partners.

VOXARN is in early development and selectively engaging with investors, manufacturers, operators, and research institutions who understand what autonomous assembly intelligence changes.

If you have a physical assembly or maintenance problem that current automation can’t solve — or you want to be part of building the category — let’s talk.

Investors / VC
Manufacturing
Maintenance & Repair
Defense
Research
PARTNER INQUIRY FORM
✓ Inquiry received.
Reference ID: —
UNITS 08
MESH_FACES 24,816
PLAN_MS 11
GPU NOMINAL
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VXN-2026-001 · REV.A