Robotics product intelligence
Understand how robots are used in the field. Improve your product. Predict failures before they happen.
Patrol mode usage after FW 2.4
Motor drift detected before breakdown
robovis> analyze fleet --period 30d
Voice recognition: 66% abandon rate after 3.2 retries.
5 robots used as surveillance cameras 22:00-06:00.
Recommend: auto-fallback touch UI + night mode.
Problem
Manufacturers lack Mixpanel-level visibility. Operators discover failures too late. Critical environments cannot rely on the cloud.
Which features drive adoption? Where do users abandon? Which environments create friction?
Which robot is about to fail? What should maintenance do this week? Which threshold needs action now?
How do you get AI-driven insight without sending sensitive robot data to the cloud?
Two products
RoboVis gives manufacturers product intelligence and gives operators predictive maintenance.
PRODUCT INTELLIGENCE
Understand adoption, journeys, friction and emerging usage to improve the robot itself.
PREDICTIVE MAINTENANCE
Know when a robot will fail before it does. Reduce downtime and protect fleet operations.
Use cases
Three industries, three sets of problems. One platform that solves all of them.
The problem
Navigation retries spike at junction points during peak hours. Battery drain is unpredictable between wards. The room-mapping feature is barely used despite significant development cost.
RoboVis detects
Override rate at corridor junctions — retry pattern detected
Drift signature detected — maintenance recommended within 5 days
The problem
Silent comms dropouts occur in urban canyons with no alert. Thermal sensor adoption varies by crew and shift. Mission aborts happen but root causes are never analyzed systematically.
RoboVis detects
In cold environments — feature used as designed. 31% in temperate zones.
Pre-failure vibration pattern — field replacement scheduled
The problem
Pick path efficiency degrades during night shifts with no visibility. Charger docking retries create bottlenecks that delay entire shifts. Barcode scanner retries spike on certain SKU zones.
RoboVis detects
Pick efficiency gain identified after night-shift path recalibration
Early drift signature — schedule inspection before peak season
How it works
Compact summaries on the robot. Structured intelligence in Central. Local LLM insight on top.
A Rust runtime extracts features, detects anomalies and tracks usage in real time on the robot.
Only compact reports are uploaded and stored. No raw continuous stream required.
Ollama turns operational and product signals into recommendations, predictions and summaries.
Key features
Know which robot capabilities are loved, ignored, or constantly overridden.
Track session flows, repeated paths and abandonment moments.
Identify retries, failures and operator workarounds automatically.
Compare usage across sites, firmware versions and customer segments.
All insight generation runs locally with no cloud dependency.
Surface drift, pre-anomaly context and imminent failure risk before downtime.
Security
No internet required at runtime. Local LLM, local DB, local deployment.
Each robot can authenticate with secure mutual TLS when transport is enabled.
Aggregated metrics, no raw sensitive payloads by default, human validation for config push.
Customers decide what is shared with the manufacturer: none, aggregated or full.
Integration
Sensor adapters let RoboVis ingest IMU, encoders, battery, proximity, touch, audio and more.
Request a demo
Landing page, manufacturer dashboard, operator dashboard, all on your infrastructure.