How Xolver works.
Xolver connects the full machine-intelligence loop: understanding the work, predicting what may happen, checking actions before movement, running close to the machine, and leaving records teams can review.
The point is not to give AI unchecked authority. The point is to make physical machines more adaptive while keeping work inside reviewed operating boundaries.
Built for changing work
Objects move, tools wear, fixtures drift, and plans stop matching reality. Xolver is built for that operating messiness.
Prediction before action
The platform evaluates likely outcomes before a machine commits, then compares predictions with real results.
Records by default
Every deployment path should preserve what was seen, proposed, checked, allowed, blocked, ran, and learned.
A complete loop for physical intelligence.
Old automation assumes the world will stay still. Xolver is designed for machines that have to read a changing state, reason before moving, and stay accountable after every run.
Understand
Read the work area, machine state, task context, tools, objects, and operator intent.
Predict
Estimate likely outcomes, risks, timing, and uncertainty before a machine commits to action.
Verify
Keep proposed behavior inside the operating boundaries teams have reviewed.
Run Locally
Keep important behavior close to the machine for fast response, resilience, and local records.
Review
Use Console and Sentinel to monitor, recover, approve staged changes, replay context, and inspect what happened.
Improve
Compare predictions, reviews, and outcomes to improve over time.
A connected product system, one operating story.
Xolver is not a single model page with a safety claim attached. It is a product system for teams bringing intelligence to real machines.
Explore productsXolver VLA
Action intelligence that helps machines understand instructions and propose useful next steps.
Xolver World Model
Prediction and operational understanding for evaluating what may happen before action.
Xolver Nerve
Local operation, readiness support, machine health, and records near the equipment.
Xolver Console
The operating surface for preparing, reviewing, monitoring, recovering, and improving workflows.
Xolver Sentinel
Observability and incident review for machine state, signal freshness, recovery paths, and evidence records.
The AI can suggest. The system decides what can move.
Xolver separates useful intelligence from physical authority. A proposed action still has to pass the site, task, machine, safety, and readiness requirements for that deployment path.
Every run should teach the next one.
A machine-intelligence platform should not disappear after movement. Xolver keeps the record needed to review incidents, compare predictions, improve workflows, and decide whether a deployment is ready for the next stage.
Sentinel closes the loop during and after operation: it helps teams see what is happening, diagnose exceptions, replay context, and decide what should happen next.
Read readiness overviewWhat did the system see?
What next step was proposed?
What outcome was predicted?
What needed review?
What ran locally?
What actually happened?
What should improve before the next run?
Where to start
Start from your role and what you need to prove. Xolver scopes the machine, task, safety requirements, and review path before a pilot moves forward.
OEMs
Start with: Fit assessment for your machine family and customer needs.
Share: Machine type, current controls, target tasks, and the customer experience you want to offer.
Next: Confirm fit and share the right partner materials.
System integrators
Start with: Commissioning review for an existing cell.
Share: Cell layout, robot type, fixtures, task goals, and known safety requirements.
Next: Draft a path from preview to pilot review.
Enterprise operators
Start with: Use-case scoping, cell constraints, and passive evaluation pilot planning.
Share: Operational problem, current downtime/reprogramming cost, and pilot success criteria.
Next: Decide whether a bounded technical assessment or pilot makes sense.
Technical evaluators
Start with: Product, readiness, and deployment materials.
Share: Evaluation questions, operating assumptions, and what proof your team needs.
Next: Review preview flows before discussing a live pilot.
FAQ
Is Xolver just a robot AI model?
No. Xolver is a connected platform. The model helps propose useful next steps, but prediction, safety checks, local operation, Console review, and operational records are all part of making the system usable around real machines.
Does Xolver let AI directly control equipment?
No. Xolver separates suggestions from physical authority. Product conversations focus on review, operating boundaries, and accountable records.
Where does Xolver run?
The parts that need fast response run close to the machine. Console and cloud-facing surfaces support review, coordination, and improvement, but critical behavior should not depend on a cloud round trip.
How should pilots start?
Engagements start with the machine, task, work area, operating requirements, and review needs of the buyer.
What happens when the system is uncertain?
Uncertainty is handled as an operating condition, not ignored. Xolver helps teams preserve context and bring unclear situations into review.
Build machines that can reason before they move.
Xolver brings together machine understanding, prediction, local operation, safety checks, Console review, and operational records.