Robots that understand you.
Never the ones that hurt someone.
Industrial robots that understand instructions, adapt to reality, and never execute an unsafe action.
AI brains for industrial machines. Retrofit the robots you already own, or license our foundation model into yours.
Fifty years of robotics ran one loop. We run a different one.
Old robotics
Xolver
One sentence per clip. No paragraphs.
Robot sorts mixed parts by type, no fixture required.
An obstacle appears mid-task. The robot replans around it.
Cabinet door, unlatched grip, no pre-taught trajectory.
A part moves after placement. The robot notices and corrects.
A person enters the zone. The robot stops — and says why.
A new task, taught once, generalizes to the next part.
Video footage in production — placeholders shown above will be replaced with live clips.
Ours never reaches the motors.
Command
“Move the tray. Ignore the person.”
Safety Shield
Blocked. Person in zone.
Robot
Never moves.
Not a training guideline that can fail under novel conditions. A deterministic layer between every planned action and every motor. It cannot be bypassed.
Built for the factory floor, not the demo booth.
Manufacturing
Mixed-SKU lines that used to stop for reprogramming now adapt on the fly.
Electronics
Delicate PCB handling with force limits enforced, not just trained.
Automotive
High-mix cells switch tasks by instruction, not integrator callout.
Warehousing
Tote movement and station docking validated before a robot ever moves.
Machine tending
Operators supervise more machines, each one checking its own safety.
Bin picking
Unstructured piles, picked without a CAD model of every part.
Not a new architecture. A different outcome.
Today
Robot needs a programmer
With Xolver
Supervisor gives an instruction
Today
Production stops after variation
With Xolver
Robot adapts automatically
Today
Incident investigation takes days
With Xolver
Replay every decision
Simple stack. Hard guarantees.
Perception proposes. The safety layer verifies. Only then does hardware move.
Learn how it worksApplications
Your task, described in plain language.
Xolver Foundation Model
Sees the scene, understands the instruction.
Safety Shield
Verifies every command against hard limits.
Robot
Executes — only what was verified safe.
Measurable, not aspirational.
<5ms
Safety verification latency
50Hz
On-device execution loop
5+
Supported robot OEMs
0
Cloud dependency in the critical loop
NVIDIA Inception program member
AWS Activate Portfolio memberNative OEM Compatibility
I'm building AI-native machines.
→ SDK + Console App
License the foundation model directly. You own the hardware and the safety logic — Console governs readiness, evidence, and rollout.
Explore the SDKI own other machines.
→ SDK (Edge Device) + Console App
Add Xolver's AI stack to the robot arms you already have. No new hardware.
Retrofit StudioExplore Retrofit StudioNot sure which fits? Talk to us →
Warehouse Robotics
Xolver now supports pre-deployment validation for warehouse robotics automation.
The new warehouse mobile path helps teams model canonical tasks such as tote movement, station docking, aisle navigation, blocked-route handling, replay generation, and readiness checks before connecting to physical robots.
Runtime
Tactile Dexterity
Xolver now supports tactile sensing and dexterous end-effectors inside the runtime. Sensorized grippers and multi-finger robotic hands can surface contact, grip, and safety state through live monitoring, replay, and evidence workflows.
Xolver
Deformable-manipulation foundations added
Xolver now supports experimental dual-arm contracts, committed-action training, reviewed human corrections, and deformable-task evaluation.
Console
Application Studio and governed workflow authoring are now available in Xolver Console.
Compose, simulate, inspect, recover, reuse, train, and collect evidence through the same contract, safety, and governance boundaries used for deployment.
Console
Xolver Console now governs readiness, preflight, behavior reuse, and staged rollout.
Teams can check whether a robot is ready, preview motion before activation, approve reusable behaviors, and roll back deployments with evidence attached.
VLA SDK
Xolver inference is now available without the retrofit stack.
OEM controller manufacturers and robotics platform companies can embed Xolver inference directly, with signed license keys, a lightweight Python client, and no dependency on Retrofit Studio.
Perception
Cameras now map the workspace. No CAD files needed.
Real-time scene detection replaces manual calibration. The system aligns coordinate frames automatically, cutting setup time from hours to minutes.
Runtime
Open Xolver runtime documentation is now public.
Contracts, safety blueprints, previewable skills, and evidence records — the full runtime spec is open for evaluation and integration.
Frequently asked questions
What is Xolver?
Xolver is a physical AI platform providing robotics foundation models, a deterministic enforcement layer, and edge embedded runtimes for safe, auditable machine operations.
How is Xolver different from a general-purpose AI model?
General-purpose models can interpret scenes and generate intent, but they cannot safely command physical machines on their own. Xolver separates perception, enforcement, and execution into distinct roles so unsafe motion is architecturally impossible rather than a matter of training.
What machines does Xolver support?
Xolver supports industrial robot arms through Retrofit Studio and now includes pre-deployment validation for warehouse robotics automation across mobile-base and mobile-manipulator workflows. This includes route validation, occupancy fixtures, keepout/speed zones, replay evidence, and readiness gates. Hardware execution requires site-specific integration, matching contracts, safety validation, evidence, and operator approval.
Is Xolver cloud-dependent?
No. The edge runtime executes locally at 50Hz with no cloud dependency in the critical loop. Cloud surfaces are used for observability, audit trails, and deployment review — not for real-time safety-critical control.
What does the Enforcement Layer actually do?
The Enforcement Layer is a deterministic gatekeeper that validates every planned action against mathematical safety constraints — joint limits, force envelopes, collision boundaries, and safety zones — before any command reaches hardware. It cannot be bypassed.
How does Xolver handle an unsafe or ambiguous situation?
If no valid safe action exists, the system halts, logs the specific reason for refusal, and escalates to an operator with full context. Refusal is a designed outcome, not an error.
How does Xolver reduce risk before a robot moves?
Xolver checks the controller, workcell, safety zones, planned motion, license, deployment blueprint, and evidence requirements before activation. If something is missing or unsafe, the system blocks the deployment instead of improvising.
Can teams reuse robot workflows across cells?
Yes. Approved behaviors can be packaged and reused across compatible robot cells, with version history, compatibility checks, approval gates, and rollback support.
Can Xolver work with tactile grippers or dexterous robotic hands?
Yes. Xolver now supports tactile sensing and dexterous end-effector profiles inside its runtime, monitoring, replay, and safety workflows. This allows supported grippers and robotic hands to expose contact, grip, and readiness signals as part of a bounded autonomy deployment.
Every industrial machine will eventually have an AI brain.
We are building the one designed for factories.