Keep physical AI visible, reviewable, and accountable.
Xolver helps industrial teams understand what machines are doing, why decisions were made, and what changed between runs.
The runtime story is simple: AI-assisted work should be inspectable before, during, and after operation. Xolver connects task context, review, operating boundaries, and records into one product experience.
A product layer for reviewed machine work.
Xolver keeps machine behavior understandable without publishing integration recipes or exposing facility-specific methods.
Understand
Capture the task, context, and intent behind the work.
Review
Show proposed behavior in a way operators can inspect.
Bound
Keep behavior inside the facility's approved operating limits.
Record
Preserve decisions, interventions, approvals, and outcomes.
Improve
Use records to understand failures and improve future work.
Detailed runtime mechanics are shared directly during qualified technical conversations.
You define the operating boundary. Xolver makes it reviewable.
Teams need a clear way to describe what a machine is supposed to do, which conditions matter, and when human review is required.
Xolver turns that context into a reviewable product workflow for operators, integrators, and stakeholders.
The public website describes the product posture. Facility-specific details stay in private engagement materials.
What teams review
Every move is filtered before it reaches the machine.
Xolver is positioned around reviewed, bounded operation. Proposed behavior should be understandable before it affects a physical workflow.
If context is unclear, teams should have a clear record, a clear review path, and a clear way to decide what happens next.
Every block is recorded
Review records help teams understand what was proposed, what changed, and why a workflow required attention.
The Xolver Console
The Console is where teams prepare, monitor, and review AI-assisted machine workflows. It keeps the product experience clear without turning the public site into an integration manual.
Prepare
Capture the application context and the operating intent in a product workflow.
Review
Make proposed behavior understandable to the people responsible for the work.
Improve
Use operating records to explain failures, compare runs, and support better decisions.
Replay any run. Understand exactly what happened.
Every run is recorded in full. You can replay it frame by frame and get clear answers to questions like:
- What was the robot asked to do?
- What setup was active when this run happened?
- What did the AI try to do, and what did the safety check change?
- What approvals were in place before this ran?
Records support review, audit, and operational learning without exposing private implementation details on the public site.
Built for real facilities, not just labs.
Xolver is designed for real facilities, where operators need clear context, reliable review paths, and records that support accountability.
Each engagement is shaped around the specific work, equipment, team, and review needs of the facility.
Built around clear review
Xolver helps teams make application context, operator review, and operating records visible before AI-assisted work becomes part of a facility workflow.
Note: Public pages describe product value at a high level. Detailed facility review materials are shared directly.
What the public site avoids
- Private integration methods.
- Facility-specific limits or operating recipes.
- Controller details or setup instructions.
- Internal implementation mechanics.
- Feature-by-feature launch claims.
Product value is public. Implementation detail is private.
Who this is for
Xolver is for teams taking AI from the lab to real factory floors — where safety, traceability, and operator control aren't optional.
Xolver handles the space between "the AI knows what to do" and "the machine does it safely."
Talk to us about your deployment
Whether you're evaluating Xolver for an existing facility or building something new — get in touch and we'll walk through what a deployment looks like for your specific setup.
Write to us at
hello@xolver.aiFAQ
Does review mode move the robot?
No. Review mode is for understanding proposed behavior before any physical action is considered.
What gets recorded for every run?
Teams can review task context, decisions, interventions, approvals, and outcomes in a clear operating record.
Can Xolver certify my robot automatically?
No. Certification and sign-off remain specific to each facility, task, machine, and operating environment.
What if my network goes down?
Xolver is designed for on-site operation so important review and operating records are not dependent on a public cloud round trip.
Does Xolver work with my existing robot?
Fit depends on the robot, controller, task, facility, and operating boundary. Xolver starts with an application review.
How does this fit with a retrofit project?
Xolver can help teams evaluate how existing equipment, operating records, and review workflows fit together.