3D Annotation
Cuboids, point clouds, object tracking, sensor fusion checks, and platform-based labeling.
- LiDAR and camera-aligned labels
- Object classes, occlusion, and state tags
- CVAT, Supervisely, Labelbox, or client tools
India-based delivery. Global robotics standards.
Physical AI data operations for robots that learn from the real world.
IndiBuying AI helps robotics, autonomous systems, and computer vision teams turn raw sensor streams into high-quality training datasets through annotation, QA, trajectory review, human demonstration labeling, egocentric capture operations, and dataset curation.
Humanoids
AMRs
Warehouse robotics
Industrial vision
Positioning
Not a generic annotation vendor. A Physical AI Data Operations Partner.
Physical AI companies need data that preserves space, time, motion, intent, and edge cases. We specialize in the operational layer between raw collection and model-ready datasets: labels, validation, error taxonomies, coverage analysis, and reporting that engineering teams can trust.
3D + video
Point clouds, cuboids, objects, actions, and trajectories
QA-first
Scoring, audits, issue categories, and corrective recommendations
India delivery
Lean specialist teams built for recurring global programs
Core Services
Dataset operations for robotics and autonomous systems teams.
Trajectory Annotation
Motion sequences that clarify how robots, humans, and objects move through environments.
- Robot path annotation
- Success, failure, and intervention labels
- Motion validation for AMRs and delivery robots
Human Demonstration Labeling
Structured labels for learning-from-demonstration and robotics foundation model workflows.
- Hand movement and pose events
- Action sequence labeling
- Object interaction and intent tags
Dataset QA
Independent validation that turns inconsistent labels into measurable dataset quality.
- Annotation validation and quality scoring
- Error reports and root-cause categories
- Review loops for distributed labeling teams
Dataset Curation
Review, coverage analysis, and data selection recommendations before teams spend on labeling.
- Edge-case identification
- Scenario coverage analysis
- Model-readiness recommendations
Physical AI Data Intelligence
Future-ready reporting across video, logs, sensor data, and annotation outputs.
- Dataset health reports
- Quality and coverage scores
- Failure pattern summaries
Physical AI Data Collection
Egocentric data operations for robots learning human-scale tasks.
First-person data captures the exact view, timing, hands, tools, objects, and contact moments involved in real human work. That makes it valuable for humanoids, manipulation systems, AR assistants, and embodied AI models that need more than static images.
Capture Programs
Field data collection using wearable cameras or smart glasses, task scripts, environment checklists, consent workflows, and repeatable capture protocols.
Multimodal Sync
Organization of first-person video, audio, IMU, depth, pose, timestamps, robot logs, and environment metadata for downstream training and evaluation.
Hand-Object Annotation
Labels for hands, active objects, tools, contact frames, pre/post conditions, state changes, task phases, success, failure, and recovery moments.
Privacy Review
PII flagging, face/license/brand review, sensitive-scene filtering, redaction queues, and data acceptance checks before annotation begins.
Ideal Customers
Built for teams shipping autonomy into physical environments.
Robotics Startups
Humanoid robotics, warehouse automation, AMR manufacturers, manipulation systems.
Computer Vision Companies
Industrial safety, retail analytics, smart spaces, factory inspection, human activity systems.
Autonomous Systems
Drones, agricultural robotics, delivery robots, autonomous vehicles, field automation.
Operating Model
A repeatable workflow from raw data to engineering-ready reports.
01
Dataset Intake
Review data types, target labels, ontology, model goals, privacy constraints, and acceptance criteria.
02
Pilot Batch
Run a controlled sample to validate guidelines, tooling, label ambiguity, throughput, and QA rules.
03
Production Ops
Deploy annotators and QA leads with weekly reports, issue tracking, and feedback loops.
04
Quality Intelligence
Deliver error categories, coverage gaps, failure patterns, and recommendations for the next collection cycle.
Engagement Models
Start with a pilot. Scale into a dedicated Physical AI data team.
Project-Based
USD 1k-5k
Small pilots, audits, labeling samples, or urgent QA projects.
Managed Services
USD 5k-15k/mo
Annotation, QA, dataset review, weekly reporting, and delivery management.
Dedicated Team
Custom monthly
Dedicated annotators and QA leads integrated with your data operations workflow.
Start a Pilot
Bring a messy robotics dataset. We will help make it trainable.
Share the data type, target model, required annotations, and timeline. We will respond with a pilot structure, operating plan, and recommended QA approach.
US, Canada, Europe focus
Delivery center in India
Built for recurring client relationships