Output 01
Physical IP
New materials and formulations, developed and validated inside the loop and ready to license.
Exper — The Product Machine
Autonomous physical experimentation for materials discovery.
A closed-loop lab where AI designs experiments, robotics runs them, and every result feeds the next iteration — producing licensable materials IP and high-fidelity physical training data.
The problem
Materials innovation is still slow, manual, and prone to error.
01
One at a time
Experiments run sequentially. A single promising direction can take months to explore properly.
02
Unrecorded variables
The conditions that quietly shape an outcome go uncaptured, and results become hard to reproduce.
03
Data lost between runs
What a lab learns in one iteration rarely survives intact into the next one.
The system
A continuous feedback loop that runs without pause.
01 — Design
AI proposes an experiment
Each round is chosen against everything the system has already measured, rather than against a fixed protocol written in advance.
02 — Execute
Robotic systems run it
Physical execution is automated end to end, so throughput stops being bounded by how many hours a technician is in the building.
03 — Capture
Sensors record everything
Including the variables most labs overlook — ambient temperature, fine-grained growth metrics, the conditions that turn out to matter.
04 — Learn
Results shape the next round
Every measurement narrows the search. The loop restarts immediately, carrying the full history forward with it.
Because the loop runs continuously and with minimal human intervention, it generates more reliable data at a far higher rate than a traditional lab.
What it produces
The system produces two outputs.
Output 02
Training data
Structured, real-world experimental datasets for AI companies building models of the physical world — a resource that is scarce today.
Pilot programs
Two first test cases.
Pilot 01
Mycelium
Fungal biomaterials — three-day growth cycles
Three-day growth cycles make fungal biomaterials a strong first test case for rapid, continuous iteration. Where a conventional programme might see a handful of generations in a quarter, the loop works through dozens.
Short cycles change what is worth trying. When a result comes back in days rather than months, exploratory experiments stop being expensive, and the system can afford to test directions a human programme would never budget for.
Growth is also richly measurable. Density, rate, morphology and substrate response all give continuous signal rather than a single end-point reading, which is exactly the kind of data the model needs to improve.
Pilot 02
Nanoceramics
Geothermal silica byproducts — advanced ceramics
Silica byproducts from geothermal energy plants are being developed into advanced ceramic materials, turning an industrial waste stream into feedstock.
The input is abundant, inexpensive and already produced at scale — an unusually good match for a system whose advantage depends on running a very large number of physical experiments.
It also sits in a different regime from mycelium entirely: inorganic, high-temperature, structurally measured. Running both on one rig is how we prove the infrastructure is general rather than tuned to a single substrate.
Why now
Two curves meeting.
Pressure 01
AI models are running out of original, high-quality data about how the physical world actually behaves.
Pressure 02
The materials sector needs faster and cheaper paths to discovery than it has today. LLMs can process information at a scale that remains largely untapped by the manufacturing sector.
Exper addresses both needs from the same infrastructure.
Let's build the loop together.
Licensing, data partnerships, or a conversation about what the system could run next.