— Platform
A complete data infrastructure layer for robotics teams.
dexset helps teams move from data requirement to real-world capture, annotation, validation, and ML-ready dataset delivery.
— Architecture
From task brief to model training.
01
Task Brief
02
Capture Operations
03
Raw Multimodal Data
04
Annotation Layer
05
Quality Review
06
Dataset Delivery
07
Model Training
08
Teleop Feedback Loop
— Delivery Console
From capture to ML-ready datasets, in one view.
Dataset delivery dashboard · JSON / COCO / YOLO / Pascal VOC / CSV / Custom
— Capabilities
What the platform handles for you.
Task definition system
Structured briefs covering objects, environments, angles, actions, and success criteria.
Capture network
Trained operators and capture setups across commercial, industrial, and residential environments.
Dataset structuring
Multimodal organization across video, frames, sensors, metadata, and task sequences.
Annotation workflows
Robotics-specific labeling pipelines with multi-pass human review.
Quality validation
Coverage, precision, completeness, and variation scoring on every batch.
Export & delivery APIs
Versioned, signed dataset delivery in the schema your pipeline expects.
Teleoperation feedback loop
Intervention capture that turns deployment into a continuous data source.
Security & traceability
Source, capture, task, environment, and annotation records for every dataset.
Human-in-the-loop review
Physical-task context applied at every quality gate, not just spot checks.
— 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
★★★★★
“Coverage scoring meant we knew exactly what variation we were missing before training, not after burning a GPU budget on it.”
Sofia Almeida
Perception Engineer, AI Robotics Company
★★★★★
“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.
What does the dexset platform cover end to end?
The platform runs the full data lifecycle: structured task briefs, capture operations, multimodal data organization, robotics-specific annotation, quality validation, and versioned delivery through APIs — plus a teleoperation feedback loop that turns deployment interventions into new training data.
Can we monitor dataset progress during a project?
Yes. The delivery console shows every dataset with clip counts, frame counts, quality scores, review status, and available export formats, so your team can track capture and annotation progress in one view rather than waiting for end-of-project handoffs.
How does quality validation work?
Every batch is scored for coverage, annotation confidence, variation, and completeness before release. Error flags are resolved in human review, and datasets only reach delivery status once they pass the thresholds agreed in your task brief.
Does the platform integrate with our training infrastructure?
Yes. Datasets are delivered through versioned, signed APIs or direct cloud-storage transfer, with schema mapping defined once and applied to every release. Webhooks can notify your pipeline when a new batch passes review.
See the platform on your task.
Bring one workflow your robot needs to learn. We will show you how it becomes a dataset.