Parlon Solutions for Robotics
Most robotics teams find out about a coverage gap only after a robot already stopped.
Infrastructure has changed. Observability hasn't.
A fleet depends on a wireless or private 5G path most robotics teams don't own and can't fully see: an access point handoff, a shift in racking, an edge compute node the perception stack runs on. A site survey done once, before a fleet ever ships, can't keep up with a floor that never stops changing. Parlon gives fleet and operations teams one continuously validated view of the path a robot actually depends on, at every site, before a connectivity gap becomes a stalled robot.
Fleet adoption and connectivity figures: ABI Research, 2026 · industry-reported robotics and warehouse-automation data, 2026.
See it in 30 seconds
What continuous coverage validation looks like across a live fleet.
A 30-second look at coverage validation across the same floor space a robot fleet actually travels.
Robotics & Warehouse Automation Solutions
Built for a floor that never stops moving.
AI-enabled perception, private wireless dependency, and a fleet that scales facility by facility all create the same exposure: a site survey done once can't validate a floor that keeps changing. Three entry points, one platform underneath. Start where your problem is.
See, govern, and optimize the infrastructure powering AI.
Replace without compromise: complete coverage, modern capabilities.
Detect and resolve problems before they impact users, revenue, or trust.
What you already know
The robot's perception and path-planning stack is an AI workload too. It just runs on the floor, not in a rack you can walk into.
Perception, path planning, and increasingly foundation-model-driven decision-making run onboard the robot or at the network edge, not in a GPU cluster anyone can walk into and inspect. That workload depends on the same wireless or private 5G path a bad handoff or a degraded access point can quietly break. Observability built to watch a datacenter was never built to watch a moving AI workload spread across a warehouse floor.
EMA Network Management Megatrends 2026 · GlobalData, 2025, as cited in RCR Wireless.
What Parlon does
- 01Device-level edge compute health. Utilization, thermal throttle, and error rates on the single onboard or edge device a perception model actually runs on, not a facility-wide average.
- 02Power as a first-class signal. Breaker-level load, feed redundancy, and capacity runway, wherever that compute runs in infrastructure you own.
- 03Agent activity, attributed. Every AI agent read tied to a known identity and audit-logged, relevant for any model making decisions on the floor.
- 04Fabric path, hop by hop. Every hop along the wireless fabric a robot depends on, tested continuously, not surveyed once.
The first question worth answering Can you see where signal quality is degrading across every site your fleet depends on, before a robot finds out?
Scope the fleet connectivity gapWhat you already know
A site survey is a permission slip. It was never built to keep up with a fleet that moves racking as often as it moves robots.
Most fleets are validated once, at deployment, with a site survey that captures a single moment before racking, inventory, and access points ever shift. After that, access-point monitoring only tells you something is wrong once it's already down. Neither answers the question that actually matters to a moving fleet: does the path still hold up right now, on the floor robots are actually crossing today?
Industry-reported, RCR Wireless, 2025. Network infrastructure upgrades often run $30,000–$150,000 per facility and are rarely included in automation vendor quotes (industry-reported, 2026).
What carries over, what gets replaced
- ✓Device coverage and certifications. 250+ vendors. Nobody re-cables a network or recertifies a device to run Parlon.
- ✓On-premises operating maturity. Deployment history in regulated and industrial environments carries straight over.
- →The point-tool sprawl. One normalized data model instead of correlation across four to ten consoles.
- →Alert volume and admin load. Alert Auto-Tune™ recommends thresholds with evidence and a human approves them.
The first question worth answering How would you know, right now, whether the path still holds up at the site you deployed to eighteen months ago?
Build a replacement caseWhat you already know
Less than 10% of the connectivity problems on your floor ever reach IT. The other 90% show up as a robot that stopped.
Metal racking, moving inventory, and structural obstructions all interfere with signal propagation. A robot moving between coverage zones can briefly lose connection during a handoff, or drift into a zone where signal strength has quietly degraded, and most of that never generates a ticket before a robot stops where it is, waits to reconnect, or enters a safety state. Active validation tests the same floor space a robot actually travels, continuously, not once.
ConnectRF, industry-reported, 2026 · EMA Network Management Megatrends 2026, n=352.
What Parlon does
- 01Native synthetics, not a module. Latency, coverage, and handoff analysis in the same system as the telemetry, run against the actual floor space a fleet travels.
- 02Active tests within hours. Including inside fully air-gapped or private-wireless environments.
- 03LLM-aware workflow checks. Validate the AI-dependent perception and path-planning path, not only the network beneath it.
- 04One data model behind both. A synthetic result and a device metric correlate without a swivel chair, on the same floor a robot is crossing right now.
The first question worth answering How long after a change do you know the path still works, before a robot finds out it doesn't?
Run a synthetic validation testOne platform underneath
Three routes into the same data model.
The routes above are entry points, not products. Nothing here is a module, a bolt-on, or a second console.
Normalization at ingest
Every source mapped to a unified schema the moment it arrives, with vendor detail preserved. Correlation is immediate rather than reconstructed.
Synthetics and telemetry, unified
Active testing and continuous collection in one native system, including LLM-aware workflow checks.
Alert Auto-Tune™
Threshold recommendations with the evidence behind them, approved by a human. When Parlon alerts, it is worth acting on.
Deploy anywhere
SaaS, on-premises, hybrid, and fully air-gapped, with customer-controlled boundaries. In production today.
Evidence, with the record attached
One deployment, and what it does and does not establish.
Global warehouse robotics manufacturer, live fulfillment floor
An AI-enabled mobile robot fleet, picking, storage, and retrieval, depends on continuous wireless coverage across warehouse floors spanning hundreds of thousands of square feet. The team had been validating that coverage with a point-in-time site survey and passive access-point monitoring that only flagged an outage after it happened, neither of which showed where signal quality was quietly degrading before a robot encountered it. Parlon deployed continuous coverage validation across the same floor space the fleet travels, live, on operational floor space.
The evidence record
What this result establishes
- Where observed: one proof-of-value deployment, global warehouse robotics manufacturer, live fulfillment floor.
- How measured: continuous synthetic coverage testing against the fleet's actual operating footprint, compared to the site's prior site-survey-and-passive-AP-monitoring approach.
- Configuration: live operational floor space, hundreds of thousands of sq ft, AI-enabled mobile robot fleet (picking, storage, retrieval).
- What it does not establish: a realized cost or staffing reduction (this deployment is proof-of-value complete; telemetry monitoring, normalization, and alert auto-tuning are planned additions, not yet measured), or a result at a site running private cellular instead of Wi-Fi.
- Named-use authority: anonymized here by agreement.
No logo wall, no generic ROI calculator. Every number on this page carries a record like this one.
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