AI needs Eyes and Ears, before they think can.
AI agents, autonomous fleets, and Physical AI do not fail due to algorithms – they fail due to data. Seconity gives AI eyes and ears on the shop floor, creating a trustworthy real-world data foundation.
Live demo: Natural language queries on real manufacturing data.
From sensor to autonomous decision. In four stages.
Every AI is only as good as the operational data it receives. Before any model can optimize, predict, or act autonomously, it must be able to perceive physical reality.
Eyes
Cameras, LiDAR, RTLS, Computer Vision, asset tracking, material flow capture. Everything that makes position and movement visible.
Ears
RFID, BLE/UWB, PLC events, machine signals, sensors, worker interactions. Anything that reports states and events.
Real-world data foundation
Structured perception: spatial intelligence, process context, event chains, the digital thread – normalized, time-coherent, deterministic.
Predict, optimize, automate
Only now does the actual AI begin: predictions that are accurate because the input data is accurate. Autonomous processes tuned to the present.
“See everything. Hear everything. Understand everything. Act autonomously. In this order – and no stage can be skipped.”
AI is not an add-on module for us. It is built in.
You don't have to wait for the autonomous factory to benefit from AI. Seconity One brings AI features that work from the very first use case.
Anomaly detection
Movement patterns that deviate from normal are detected before anyone even looks for them.
Predictive ETA
When will replenishment actually arrive? Forecasts based on real lead times instead of planned values.
Process mining without tagging
Your actual processes reconstruct themselves automatically from location data—including detours.
Natural Language Queries
“Where are all the fixtures for order 4711?” – ask the platform like a colleague.
MCP server interface
External AI agents access the twin in a standardized manner. The platform becomes the data source.
APS Feed
Real-time planning parameters directly from physical reality – input data that makes optimization truly meaningful.
The difference between data that powers AI, and data that deceives AI.
Manual data entry – scanning, acknowledging, reporting – generates data that is delayed, incomplete, and systematically distorted. A model trained or planned on this learns a shop floor that doesn't exist.
Automated capture produces the opposite: time-coherent, deterministic, seamless event data. Every event carries a real timestamp, a real position, a real context.
That's why Level 1 – eliminating manual steps – is not just an ROI program. It is building your AI foundation. Every scan eliminated today is a valid data point for the AI of tomorrow.
Evaluate your AI readiness. With facts.
Wir zeigen, welche Datenqualität AI-Anwendungen in der Fertigung brauchen, wie sie entsteht und in welcher Reihenfolge sich der Aufbau rechnet.