Use cases
Real tasks, run end to end.
An ongoing series of real-world use cases, each recorded start to finish on ordinary hardware: the document, the workflow, the questions, and the answers. Proof over promises.
Real-time learning 1
An airline changes its travel policy mid-shift. Senua AI learns a memo, applies it, then adapts to a contradictory amendment in seconds: grounded, and with no retraining.
Knowledge → Learn · Console UI
Autonomous drone flight
Speak the mission; Senua AI drafts the flight plan and commands a real autopilot in real time : on the drone’s own arm64 CPU, no model, no GPU. Deploys bare-metal via SenuaOS; syncs the fleet over RF.
Mission Control · ArduPilot SITL · Nexus-over-RF
Industrial automation
Type a plant action in plain language; the console drafts a visual control sequence and runs it against a real PLC: every write proven by the device’s own readback, on-prem and offline, no vendor SDK.
Automation editor · OpenPLC · Modbus TCP
Perception
Voice
The same learning substrate that processes every other signal, applied to audio: generating voice, understanding commands, and crossing a contested radio as intent rather than sound.
Cognition
Mathematics
The same learning engine, pointed at the movement of numbers. Arithmetic, rotation groups, and algebra learned from scratch in four days. No calculator coded, no formula authored, exact or honestly silent.
Developer platform
Build on it
Senua AI is one HTTP API. The console, the command line, and your own code all call the same routes, so integration is a web request, not a library you have to adopt.
Series
Real-time anomaly detection
One engine, four industries: Senua AI learns what normal looks like from a live data stream and flags genuine departures with an explanation: on-prem or on-device, no GPU. Each use case is validated in the open on public datasets with known ground truth, so the claims are checkable, not marketing.
UAV telemetry
On-board detection for drone fleets: learn normal flight from the aircraft’s own MAVLink stream, flag the failure before it becomes a crash: offline, on a companion board.
Workflows · Live monitor · ALFA public dataset
Plant & equipment
Your historian already recorded every failure’s precursor. A blind audit of your own data, then live monitoring: one explained alert instead of an alarm flood.
Workflows · Live monitor · Historian data
Security operations
Learn your environment’s normal behaviour; raise few, explained, confidence-ranked alerts : deployable inside an air gap, where cloud detection cannot go.
Workflows · Live monitor · Event streams
Financial crime
Each account’s behaviour, learned continuously. Flag genuine departures with a decision file (not a score) entirely inside your own environment.
Workflows · Live monitor · Transaction streams
Have a use case in mind?
Tell us the task and the documents: we’ll show you the end-to-end run.