The intelligence layer for robotics fleets
One platform. Three views. Manufacturers improve the product. Integrators operate multi-brand fleets. Operators keep robots running — all without sending data to the cloud.
Patrol mode usage after FW 2.4
Motor drift detected — investigate before breakdown
eimdall> 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. Integrators run multi-brand fleets with no unified view. 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, on which site, across which brands, needs attention this week?
Which robot is degrading? What should maintenance investigate this week? Where is reliability risk rising first?
How do you get AI-driven insight without sending sensitive robot data to the cloud?
Mythologie nordique
Heimdall monte la garde éternelle au seuil d'Asgard. Il voit à cent lieues dans toutes les directions, de jour comme de nuit. Il ne dort jamais. Il entend l'herbe pousser dans les champs et la laine s'allonger sur les moutons.
Eimdall donne à votre flotte cette même vigilance. De l'edge à l'insight. Partout. Toujours.
Three products
Eimdall gives manufacturers product intelligence, integrators fleet operations across brands, and operators reliability intelligence.
PRODUCT INTELLIGENCE
Understand adoption, journeys, friction and emerging usage to improve the robot itself.
FLEET OPERATIONS
Operate multi-brand fleets from a single view. See what's happening across sites and vendors — without depending on any one manufacturer's cloud.
RELIABILITY INTELLIGENCE
Detect degradation before it becomes downtime. Give maintenance teams the signals and context to investigate early.
Positioning
That is Fleet Intelligence.
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.
Eimdall detects
Override rate at corridor junctions — retry pattern detected
Drift signature detected — investigate 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.
Eimdall detects
In cold environments — feature used as designed. 31% in temperate zones.
Vibration pattern drift detected — field inspection recommended
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.
Eimdall 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.
Compare fleet performance across manufacturers, not just across sites and firmware versions — built for integrators managing mixed fleets.
All insight generation runs locally with no cloud dependency.
Surface drift, anomaly context, and rising reliability risk before it becomes 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. Each tenant — manufacturer, integrator, or operator — sees only what its role is entitled to see.
Your robots. Your infrastructure. Your data policy.
Integration
Sensor adapters let Eimdall ingest IMU, encoders, battery, proximity, touch, audio and more.
Already using Prometheus / Grafana?
Eimdall publishes all its fleet metrics to a dedicated secure endpoint (mTLS + bearer token, port 9091). Your existing Grafana dashboards work out of the box — import ours or build your own.
Prometheus gives you time-series. Eimdall gives you the reliability story behind them.
Request a demo
Landing page, manufacturer dashboard, operator dashboard, all on your infrastructure.