Rare environments, rare episodes
Contact-rich manipulation in a machine shop, night shifts on a dock, sterile corridors — the environments that matter most are the hardest to access and instrument.
Partners / AI Companies
Data the real
world writes.
Models plateau on scraped video and staged homes. Magnus captures physical-world data where it’s hardest to get — live factories, construction sites, and hospitals — and streams it from fleets that never stop working.
Collection, annotation, evaluation environments, and continuous intervention data from real deployments — scoped to your data spec and delivered on your cadence.
01 / Why partner
The data you need isn’t on the internet.
Sim only goes so far
Policies that ace simulation still fail on real friction, lighting, clutter, and people. Closing the sim-to-real gap takes data from the real thing.
Evaluation is the bottleneck
Without repeatable real-world benchmarks, you can’t tell whether the new checkpoint is actually better — or just better in sim.
02 / The partnership
What Magnus puts on the table.
Collection in environments you can’t access alone.
Magnus operates inside customer facilities — factories, construction sites, hospitals — with permissions, safety, and operations already handled. You define the spec; we capture it.
Annotation built for robotics, not captioning.
Episode segmentation, intervention labeling, failure taxonomies, and task-level metadata — curated by teams that understand what robot learning consumes.
Continuous data from live fleets.
Every deployment produces interventions, recoveries, and long-tail edge cases. Partners get an ongoing stream from operations, not a one-time dump.
Benchmarks that run in the real world.
Evaluate policies in real operating environments with structured, repeatable metrics — so model progress is measured where it will be judged.
03 / The exchange
A partnership, not a purchase order.
What you bring
- •Models and learning systems that improve with data
- •A data specification: tasks, embodiments, environments, formats
- •Research feedback that sharpens what the network collects
What you get
- •Rare, real-world training data on a continuous cadence
- •Real operating environments for evaluation
- •A deployment feedback loop from fleets in production
04 / How to start
Three steps in.
Define the spec
Tasks, embodiments, environments, formats, and cadence — turned into a collection plan.
Pilot collection
A scoped capture program in live environments, delivered and reviewed against the spec.
Scale to the fleet
Continuous data and evaluation runs across the Magnus deployment network.
More partner types
Partners / AI Companies
Ready for real-world data?
Send us your data spec. We’ll come back with environments, cadence, and a pilot collection plan.