Permissioned access to real operating environments for physical AI.
FieldRights provides permissioned access to real operating environments where robotics labs and data companies can generate and own human-task data for training, grounding and evaluation.
What we do
With a combined 30yrs+ of experience of both investing in and advising the owners of real world assets FieldRights is well positioned to help robotics labs and data companies secure access to these closed environments. FieldRights structures owner-approved access to closed operating environments. Buyers bring or specify the capture method, generate the data and retain ownership. Operators provide controlled site cooperation. FieldRights structures, protects and coordinates the access. The resulting data can be used to train physical-AI models, ground and calibrate simulation, and evaluate performance against real-world workflows.
Operating environments
We provide permissioned access to task-rich environments across Central and Eastern Europe, Central Asia and the Middle East: labour-intensive manufacturing, processing, logistics and industrial workflows where human work is dexterous, repetitive and difficult to simulate.
- Manufacturing
- Food processing and packaging
- Warehousing and logistics
- Industrial inspection
- Retail back-of-house
- Cleaning and facilities operations
The data gap
Physical AI needs real-world task data.
~300,000 hours of global robot manipulation data exist, against ~1 billion hours of internet video and ~300 trillion text tokens.
Source: Bessemer Venture Partners, April 2026
Data must be generated environment by environment
Robot data cannot simply be scraped. It has to be captured through real tasks in real places.
Source: Bessemer Venture Partners
The bottleneck is access to real operations.
Leading labs need 100 million to 1 billion hours of egocentric pre-training data in the next 2–3 years.
Source: Stellaris
Control the environment, control the corpus.
NVIDIA EgoScale (February 2026): robot policy performance scales predictably with egocentric pre-training data — the first proof robotics follows LLM-style scaling laws.
Source: NVIDIA / Bessemer Venture Partners
Simulation still needs real-world anchors.
As labs lean on simulation and world models, real operating environments are what ground, calibrate and test them.
Diverse, real, consented human-task data is the input that capital, compute and simulation cannot manufacture.
Team
Philipp brings over 15 years of experience advising on and executing investments in real assets and operating businesses. He built and managed a sizable portfolio with principal capital and maintains a trusted network across private capital, operators and industrial owners in Europe and the Middle East.
Start a conversation
For robotics labs, data companies, operators and investors, we would value a conversation.