Why Physical AI needs the missing layer: FLEXOO’s keynote at AI Conference 2026 Heidelberg
2026-04-15
Physical AI is accelerating - but it needs access to the real world
At the AI Conference 2026 in Heidelberg, FLEXOO delivered the opening keynote, presented by Sieer Angar (Co‑Founder & Chairman of the Board) and Michael Kröger (Co‑Founder & Managing Director).
The central message resonated strongly with the audience: AI will remain constrained as long as it lacks reliable, scalable access to the physical world - not as a “nice-to-have,” but as an architectural prerequisite.
Across the AI ecosystem, the direction is increasingly clear: better models alone are not enough. What’s needed is a technology stack that connects data, software, and intelligent hardware, and world models grounded in physical reality. FLEXOO’s keynote connected this broader trajectory to a concrete gap that still isn’t solved at scale: the physical data layer.
The core insight: the “data‑to‑action gap”
FLEXOO framed the challenge as the data‑to‑action gap - the distance between sensing something in the real world and turning it into the right action fast enough to matter. In industrial environments, “fast enough” is often milliseconds, and late intelligence becomes useless intelligence.
The talk highlighted why Physical AI is often blocked today:
- Physical systems remain under‑instrumented and largely unlabeled
- Sensor data is frequently expensive, fragmented, and geometrically constrained
- Robots still operate “blind” at the contact point, leading to emergent stops and damage
- Battery AI lacks cell‑level signals, limiting generalization
- Infrastructure leaks can remain undetected, leading to six‑figure incidents
What FLEXOO changes: sensing + data + edge intelligence
FLEXOO’s approach is to close the gap by making physical signals capturable at scale and actionable at the edge.
1) Hardware that fits where physics happens
FLEXOO builds ultra‑thin (<200 μm), free‑form, flexible sensors that integrate into surfaces and geometries where conventional sensors cannot be placed - from battery cells and robotic grippers to cooling infrastructure and automotive surfaces.
The platform is designed for industrialization: roll‑to‑roll manufacturing and ISO 9001 series‑ready production, not a lab prototype.
2) Proprietary physical data streams as a strategic moat
FLEXOO emphasized that the value is not only in generating raw signals, but in transforming them into high‑fidelity, model‑ready datasets: calibrated, low‑noise, synchronized signals captured in positions others cannot instrument. This creates proprietary data streams that become a structural advantage for training models grounded in physical processes.
3) Domain models trained on physical reality
The keynote showcased application‑specific Physical AI domain models trained on FLEXOO sensor data - including:
- Robotics: tactile/force maps enabling contact‑aware gripping and precise manipulation (e.g., delicate assembly tasks)
- Batteries: cell‑level pressure and temperature dynamics enabling earlier failure detection and improved utilization (targeting +5% capacity utilization without cell redesign)
4) Edge deployment: intelligence inside the machine
A recurring theme was that Physical AI must run close to the sensor, close to the physics, close to the action. FLEXOO described an edge “nervous system” approach: local inference, no cloud dependency, deterministic behavior - enabling robust operation even without connectivity.
From hardware leadership to a full Physical AI system
FLEXOO’s sensors and readout electronics are already commercially deployed, and the company is now extending that foundation into a full system: hardware + proprietary data + domain models + edge deployment - an integrated stack built to power Physical AI in real industrial environments.
Sieer Angar (Co-Founder & Chairman of the Board) outlining FLEXOO’s Physical AI architecture, spanning sensing, data capture and edge intelligence.
Michael Kröger (Co‑Founder & Managing Director) presenting FLEXOO’s end‑to‑end Physical AI stack - from sensor technology to edge‑level intelligence.