Machines that understand, predict, and act.
Xolver is the intelligence platform for physical machines.
It helps robots and industrial systems move from brittle scripts to adaptive workflows that can still be reviewed, governed, and kept inside clear safety limits.
Backed by startup programs
Track the moving object and grab it at the right moment
Physical tasks, running at real speed on Xolver models.
Representative manipulation, sorting, inspection, and safety-aware runs.
All videos 1x speed
Pick each item and stack it in the target zone
Sort the objects into their assigned locations
Respect the safety perimeter while completing the move
Segregate the known parts into respective category locations and alien parts on the safety zone
Inspect each item and qualify the acceptable parts
Apply controlled tactile force to complete the task
The complete loop for physical intelligence.
Physical operations are dynamic. Xolver connects reading the world, understanding the current state, predicting likely outcomes, selecting actions, checking boundaries, acting locally, and learning from every result.
Step 1
See
Read the work area, machine state, tools, and task context.
Step 2
Understand
Build a live view of what is happening and what the operator wants done.
Step 3
Predict
Estimate what is likely to happen before the machine commits to a move.
Step 4
Act
Turn approved goals into machine behavior through controlled workflows.
Step 5
Verify
Check actions against site rules, safety limits, and equipment boundaries.
Step 6
Learn
Compare predictions, simulations, preflight checks, and real outcomes to improve the system over time.
One operating story across model, edge, and Console.
Xolver is built as connected products for teams bringing intelligence to real machines, from task understanding to local operation and review.
Understand and propose
Xolver Robotics Models
VLA and world models for physical machines.
Combines Vision-Language-Action models with world models so robots can understand instructions, read the work area, predict likely outcomes, and propose useful next steps.
- VLA task understanding
- World-model prediction
- Adaptive workflows
- Manipulation and navigation intelligence
Run and record locally
Xolver Nerve
Edge hardware and local runtime for guarded machine intelligence.
Runs important parts of the system on edge hardware close to the machine for fast response, resilience, and clear local records.
- Edge hardware
- Fast local response
- Deployment readiness
- Machine health signals
- Local records
Review and improve
Xolver Console
The command center for deploying, governing, and improving physical AI.
Gives teams one place to prepare, review, monitor, recover, and improve machine intelligence across the full lifecycle.
- Workcell review
- Prediction review
- Readiness review
- Rollouts and recovery
- Operational records
From model output to governed machine action.
Xolver separates intelligence from authority.
The AI can propose an action. Xolver predicts likely outcomes, checks the action against the operating boundaries, runs approved behavior close to the machine, and records the result for review.
This architecture allows machines to become more adaptive without giving opaque AI unchecked control over physical equipment.
01
Propose
The AI suggests a next step from task context and current state.
02
Predict
The world model estimates outcome, timing, risk, and uncertainty.
03
Verify
Safety and readiness checks decide whether movement is allowed.
04
Run
Approved behavior executes close to the machine.
05
Review
Console preserves the record so teams can improve the workflow.
A world model for real operations, not just simulation.
Xolver World Model gives machines an operational understanding of the physical system around them.
It combines current observations, machine state, work-area context, simulation results where available, and historical outcomes.
Explore Xolver World ModelOperational view
Capture the machine, tools, work area, active task, and current operating context.
Scene context
Connect what the system sees with what the task requires.
Advisory prediction
Estimate success, risk, timing, and uncertainty before action.
Candidate ranking
Compare possible next steps before selecting, slowing down, or asking for review.
Improvement loop
Compare predicted outcomes with real outcomes to measure trust over time.
Prediction is advisory. It helps rank and evaluate actions, but it does not bypass safety checks, site policy, or equipment boundaries.
Intelligence where the machine actually runs.
Xolver Nerve runs important parts of guarded physical intelligence close to the machine, where fast response, resilience, privacy, and control matter.
Local operation
Run important checks and records near the machine.
Readiness
Track whether a workflow is ready for preview, pilot, or further review.
Machine health
Monitor connection, signal freshness, and operating state.
Records by default
Keep a local record of what was seen, proposed, allowed, blocked, and reviewed.
Offline resilience
Keep local authority when connectivity is limited.
One console for the intelligence lifecycle.
Xolver Console is the command center for building, validating, deploying, governing, monitoring, and improving physical AI.
It makes the operational loop visible: what changed, what needs review, what ran, and what should improve before the next deployment.
See the ConsoleReview surface
Build
Create workflows, templates, reference applications, and deployment plans.
Simulate
Run previews and replay workflows before deployment.
Predict
Review outcome predictions, risk signals, and uncertainty.
Deploy
Prepare governed changes through readiness gates.
Monitor
Track safety state, machine health, interventions, incidents, and site status.
Recover
Handle unexpected conditions through controlled recovery workflows.
Prove
Export evidence showing what the system saw, predicted, allowed, executed, and learned.
Adaptive intelligence. Clear control.
AI should not have unchecked authority over physical machines. Proposed actions are checked against site rules, equipment limits, workspace boundaries, safety requirements, and deployment policies before they reach equipment.
When an action is invalid, unsafe, stale, or too uncertain, Xolver can block it, replan, slow execution, request review, or trigger a controlled recovery path.
Every action should leave a record.
Xolver turns machine intelligence into a reviewable operating loop. For each deployment path, Xolver can preserve what the system saw, predicted, checked, allowed, blocked, ran, and learned.
Run record
Reviewable
What did the system observe?
What action was proposed?
What outcome was predicted?
What did the safety checks allow or block?
What ran locally?
What actually happened?
What should change before the next run?
Applications for machines that work in the real world.
Xolver helps robots and physical systems handle changing work, uncertain conditions, and operational exceptions while keeping teams in control.
Visual Inspection
Help machines inspect parts, flag defects, and route uncertain cases for review.
View applicationMachine Tending
Support load, unload, orientation, handoff, and exception handling around production machines.
View applicationHandling & Kitting
Help robots pick, place, sort, kit, and load parts across changing work areas.
View applicationPrecision Assembly
Support insertion, connector mating, alignment, and recovery for delicate assembly tasks.
View applicationWarehouse Robotics
Coordinate mobile robots, routes, stations, tote movement, and operational exceptions.
View applicationData Center Operations
Connect facility signals, monitoring workflows, incidents, and reviewable operational records.
View applicationBuild machines that can reason before they move.
Xolver brings together machine understanding, prediction, local operation, safety checks, and operational records for the next generation of physical machines.
Frequently asked questions
What is Xolver?
Xolver is an intelligence platform for physical machines. It helps robots and industrial systems understand changing work, choose better next steps, check safety before movement, and keep a clear record of what happened.
Does Xolver let AI directly control equipment?
No. Xolver separates suggestions from movement. Proposed actions are checked against the site, task, equipment, and safety requirements before anything can reach physical equipment.
Where does the critical loop run?
The parts that need fast response run near the machine, so the system is not dependent on a cloud round trip for critical behavior.
What does Console govern?
Xolver Console gives teams one place to prepare, review, monitor, and improve intelligent machine workflows.