— Industries
Designed for the physical AI use cases moving fastest.
Task-specific capture, annotation, and delivery for the environments where robots are being deployed today.
Humanoid Robotics
Task demonstrations for general-purpose robots learning everyday manipulation, navigation, and human-assistance workflows.
Warehouse & Logistics
Datasets for picking, packing, sorting, scanning, shelving, and movement across logistics environments.
Industrial Automation
Workflow data for inspection, assembly, quality checks, machine interaction, and safety monitoring.
Service Robotics
Human-assistance, hospitality, cleaning, delivery, and indoor navigation datasets.
Autonomous Vehicles
Scene understanding, object detection, edge-case capture, and environment-specific perception datasets.
Agriculture Robotics
Crop monitoring, field navigation, object recognition, harvesting support, and environment variation datasets.
Healthcare Robotics
Controlled task data for assistive robotics, device interaction, mobility support, and clinical environment workflows.
Retail Robotics
Shelf scanning, product recognition, inventory checks, restocking workflows, and store navigation datasets.
— 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.
★★★★★
“dexset captured 400 hours of bin-picking demonstrations across three warehouses. The failure taxonomy alone cut our triage time in half.”
Lena Ortiz
Head of Robot Learning, 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
★★★★★
“From brief to delivered COCO export in three weeks. Generic annotation vendors quoted us three months for lower quality.”
David Park
Founding Engineer, Independent Robotics Vendor
— FAQ
Frequently asked questions.
Do you have experience in my industry?
dexset runs capture programs across humanoid, warehouse and logistics, industrial, service, autonomous vehicle, agriculture, healthcare, and retail robotics. Each industry page details the tasks, environments, and data types we capture for that domain.
Can you capture inside our own facilities?
Yes. On-site programs run under your access, safety, and compliance requirements, using our capture protocols. Where on-site capture is not possible, we reproduce your environment and task conditions in comparable or controlled settings.
How do you handle privacy in environments with people?
Capture follows consent-based protocols: briefed participants, signed releases, and where required, face blurring or exclusion zones. Every clip carries consent records as part of its traceability metadata.
Can one dataset serve multiple robot platforms?
Often, yes. Task demonstrations, scene data, and interaction labels are platform-agnostic by design. Platform-specific needs — camera placement, end-effector context — are handled in the task brief so the same program serves multiple systems.