— About dexset
Building the real-world data layer for physical AI.
dexset exists to help robotics teams train machines with data from the environments where those machines will actually work.
— Mission
Make real-world robotics data easier to collect, structure, validate, and use.
Robotics data is different. It is physical, sequential, multi-view, and full of edge cases that only appear in deployment. We built dexset around that reality, with humans in the loop at every quality gate.
Real-world variation matters.
Data must be task-specific.
Annotation needs physical context.
Every failure case is a training signal.
Robots improve through continuous data loops.
— Compliance & Trust
Compliant by design, across every dataset.
From first consent form to final delivery, dexset workflows are built to meet data protection, security, and labor standards across the regions where we capture and the regions where our customers operate.
Informed consent
Every participant is briefed and signs a release before capture begins. Consent records attach to each clip, and withdrawal requests are honored across all delivered versions.
Privacy protection
Face blurring, anonymization, and exclusion zones are applied wherever required. We minimize personal data by design and never collect more than the task brief demands.
Data security
Footage is encrypted in transit and at rest, with role-based access controls, signed delivery URLs, and per-recipient transfer records on every dataset.
Regional compliance
Capture and delivery workflows are designed to align with GDPR and UK GDPR, CCPA/CPRA, PIPL, APPI, and LGPD requirements, with regional data-residency options where needed.
Traceability & audit
Source, consent, capture, environment, and annotation records persist for every dataset, supporting audits that run from raw footage to the exported training file.
Responsible sourcing
Capture operators, demonstrators, and annotators are fairly paid and work under documented, safe conditions — quality data should never come from exploitative labor.
— What Teams Say
Trusted by robotics teams.
★★★★★
“We sent one task brief and got back a dataset that loaded into our pipeline on the first try. Custom schema, zero rework.”
Marcus Chen
ML Infrastructure Lead, Independent Robotics Vendor
★★★★★
“The egocentric hand-pose labels are the best we have evaluated. Our grasp success rate improved 18% after one fine-tuning round.”
Priya Raman
Manipulation Lead, AI Robotics Company
★★★★★
“Their teleoperation intervention capture turned our deployment into a data source. Every takeover now feeds the next training run.”
Jonas Weber
CTO, Independent Robotics Vendor
— FAQ
Frequently asked questions.
What does dexset do in one sentence?
dexset is the real-world data layer for physical AI: we help robotics teams define, capture, annotate, validate, and deliver the task-specific datasets their models need to work outside the lab.
What makes robotics data different from other ML data?
It is physical, sequential, multi-view, and contact-rich. A label is only correct in the context of a task, an object, and an outcome — which is why our pipeline keeps humans with robotics context at every quality gate.
Who does dexset work with?
Robotics startups, humanoid and logistics robotics companies, industrial automation teams, AI labs building robotic foundation models, and research groups — anyone training models that must perceive, move, and act in the real world.
How can my organization partner with dexset?
We partner with robotics teams as customers, with facility operators as capture sites, and with annotation specialists as reviewers. Use the contact page to tell us which fits you.
Work with us.
We partner with robotics teams, capture operators, and annotation specialists worldwide.