Keep machine behavior inside reviewed boundaries.
A product layer for reviewing proposed behavior before physical work changes.
AI can propose useful next steps, but physical teams need review, context, and operating records. Xolver helps keep that boundary clear.
A review boundary around machine behavior.
Xolver helps teams separate AI suggestions from the authority to change physical work. Proposed behavior stays tied to operating context and human review.
For contact-rich workflows, the same product posture applies: make context visible, keep review clear, and preserve the operating record.
Reviewed boundaries
Operating limits should be visible to the people responsible for the machine.
Clear operating context
Teams can keep workspace boundaries, restricted areas, fixtures, and policies part of the review conversation.
Safety is also about how the robot touches the world.
Safety is not only about where a robot moves. It is also about how the robot touches the world. Xolver frames contact, grip, and handling context as part of the review story for delicate physical work.
What this enables
- Safer handling of delicate or variable parts
- Better visibility into grip and contact state
- Review context for end-effector behavior
- Clear records when contact conditions need attention
AI suggestions with operating context.
Intelligence without limits is a liability in a physical environment. Xolver keeps the public product story focused on review, context, and accountability.
Can the machine actually do this?
Every proposed movement is checked against the physical limits of the machine — range of motion, operating boundaries, safe zones. If it isn't possible, it doesn't happen.
Turns AI intent into reviewable behavior
The AI describes what it wants to do in general terms. Xolver helps teams review that intent before physical work changes.
Explicit Failure Behavior
Halt. Log. Escalate.When a proposed action violates operating boundaries or machine limits, Xolver does not improvise. It can halt motion, record the reason, and route the event for review.
Fast enough to keep up. Strict enough to be trusted.
Important review signals stay close to the equipment, helping teams keep physical work responsive and accountable.
Runs close to the machine
Important review signals stay near the equipment so behavior can remain responsive without depending on a cloud round trip.
Works with any sequence of moves
Whether the AI is planning one step at a time or a whole sequence of movements, the same safety check applies at every point. No shortcuts for complex tasks.
Works with any AI model
The same review posture applies regardless of where a suggestion comes from: proposed behavior still belongs inside the operating boundary.
Bring reviewable autonomy to your operation.
Stop compromising between intelligent adaptability and clear control. Deploy machine intelligence that respects reality.
FAQ
What do Xolver safety checks do?
They help teams review proposed behavior against operating rules, equipment limits, workspace context, and site requirements.
Does enforcement make the model safe by itself?
No single product layer makes physical work safe by itself. Safety depends on the machine, site, operators, existing safety systems, and review process.
What happens when an action is rejected by enforcement?
The event should be recorded and brought to an operator or reviewer instead of being hidden or improvised around.
Can enforcement refine an action instead of blocking it?
The public story is simple: unclear or unacceptable behavior should be brought into review rather than forced forward.
How does enforcement relate to OEM compatibility packs?
Compatibility work is handled through qualified engagement. Public pages keep those details private.
Is enforcement the same as a PLC safety system?
No. Xolver does not replace plant safety systems or certified safety hardware. It adds a review-oriented product layer around AI-assisted machine behavior.