How NOBU works
Most monitoring systems wait for something to go wrong. NOBU works differently — it continuously evaluates your system from every angle, and problems surface early — often before conventional monitoring raises any flag.
NOBU vs. traditional approaches
| Traditional monitoring | NOBU | |
|---|---|---|
| Warning time | After threshold crossed | Before readings look unusual — scales with sampling rate |
| Setup | Weeks of baseline collection | Starts immediately, no training data required |
| False alarms | Frequent — static thresholds don't adapt | Significantly fewer — risk levels adapt to operating conditions |
| Confidence | Binary: alarm or no alarm | Graded risk levels with calibrated confidence |
| Adaptation | Manual recalibration after changes | Continuously adapts to operating conditions |
| New failure modes | Can't detect what it wasn't trained for | Detects emerging problems regardless of whether they match known failure patterns |
Why the difference exists
Traditional: learn what's normal
Most systems build a model of "normal" from historical data, then alarm when readings deviate. This fails on new equipment, after maintenance, or for failure modes never seen before. It also means weeks of collecting baseline data before you get any value.
NOBU: track what's uncertain
NOBU evaluates your system's condition continuously against multiple scenarios simultaneously. It doesn't need to know what "normal" looks like — it identifies emerging problems by detecting when the operating pattern starts to shift in ways that precede failures.
Warning time scales with how often you sample. At one reading per minute, NOBU typically identifies emerging problems within a handful of readings — well before threshold-based alarms would trigger. The exact lead time depends on how fast the problem develops and your sampling rate.
On confidence
When NOBU reports 85% confidence, that number reflects a rigorous assessment of your system's condition, not a black-box anomaly score. It's grounded in how consistently the sensor data matches healthy operating patterns versus developing-fault patterns.
This means the confidence level is actionable: high confidence means act now, medium means increase monitoring, low means note and move on. Over time, as NOBU sees more of your system's behavior, its estimates become more precise.
Two ways to integrate
Agent (continuous monitoring)
Install the NOBU agent on a laptop, Raspberry Pi, or industrial PC connected to your sensors. It collects data, forwards it to NOBU, and you monitor results in your dashboard. Available for Linux, Windows, and macOS.
API (on-demand inference)
For Control, API Fuzzing, and Anti-Distillation services, call the REST API directly from your application. Send sensor data, system state, or API query patterns, and get back decisions with calibrated confidence levels. Billed by compute time.
Getting started
Choose a service
PowerSense for power quality, Predictive Maintenance for equipment health, or API services for control, security testing, and model protection.
Install the agent
Download the agent for your platform, set your API key in config.yaml, and start the service. Takes about 5 minutes.
Open your dashboard
Watch NOBU learn your system in real time. First useful predictions typically appear within an hour of data collection.