Define what the model needs to learn.
Target tasks, environments, capture modalities, inclusion rules, annotation needs, acceptance criteria, and delivery format are documented up front.
Kenya-based real-world data operations
EgoHub AI builds managed data collection, annotation, quality control, and delivery pipelines for robotics, world models, and physical AI.
Beverage sorting and arrangement
Price tag replacement and storage
Flower arrangement
Install a ping-pong net
Peel and prepare garlic cloves
Replace a remote-control battery
Sort and store clean cups
Sort cutlery into a box
Tighten a hand-cream cap
Unplug a charger
Wipe a ping-pong table
Wipe a tool workbench
Embodied AI data operations
Operational data pipelines for physical AI teams.
EgoHub AI focuses on real-world embodied intelligence data, connecting managed field operations with a platform workflow built for collection, annotation, QA, and delivery.
Data specs
Inspectable data packages for robotics teams.
Every delivery can be organized around an inspectable data brief: task scope, capture protocol, assets, metadata, annotation schema, QA evidence, and a handoff manifest your team can review before training or evaluation.
MP4 clipsJSON metadataAnnotation schemaQA notesDelivery manifest
staplerleft hand00:10.8 / refill phase
delivery.jsonready
task_labelrefill_staples
camera_viewhead_left / undistorted
qa_statusview verified
handoffclips + metadata
Delivery and QA
A documented path from field capture to training-ready data.
We align the capture brief, annotation schema, review checkpoints, and handoff format before the project begins, so the resulting dataset is easier for research and engineering teams to inspect.
Keep assets and labels connected.
Clips can be paired with task, scene, object, action, viewpoint, language, and annotation-version fields as required by the brief.
Make review evidence traceable.
Capture integrity, protocol compliance, label consistency, and delivery completeness are checked with issue tracking and QA notes where applicable.
Deliver a package your team can navigate.
Assets, metadata, annotation files, QA status, known issues, and dataset versioning can be supplied through a clear manifest.
Capabilities
From messy field reality to training-ready datasets.
Real-world data collection
Managed field teams capture physical AI data from urban, indoor, mobility, retail, and human-task environments across Kenya.
Annotation for embodied systems
Task labels, scene context, object states, action traces, and quality notes prepared for robotics, world models, and multimodal AI.
Quality control operations
Layered review, sampling, issue tracking, and delivery checks keep datasets useful before they reach model training teams.
Structured delivery
EgoHub Data Platform organizes collection briefs, annotation workflows, QA evidence, and export-ready data packages.
Data categories
Built for robotics, agents, and world models.
Robot task demonstrationsHuman activity and instruction dataIndoor and outdoor scene captureRetail, logistics, and facility workflowsMultilingual speech and field notesImage, video, sensor, and metadata review
Retail shelf operations
Beverage sorting and arrangement
Multi-step assembly
Install a ping-pong net
Food preparation
Peel and prepare garlic cloves
Device maintenance
Replace a remote-control battery
Kitchen organization
Sort and store clean cups
Industrial workspace cleaning
Wipe a tool workbench
Video cases
30 first-person tasks captured in motion.
Retail shelf operations
Beverage sorting and arrangement
First-person shelf work capturing bottle pickup, category sorting, placement, and front-facing product alignment.
Point-of-sale restocking
Checkout gum replenishment
A compact retail replenishment task with package handling, display inspection, and precise checkout-shelf placement.
Retail fixture maintenance
Price tag replacement and storage
A multi-step store workflow covering label removal, replacement, verification, and storage of the used tag.
Product alignment
Align shower gel bottles
Fine shelf-facing behavior with bottle grasping, orientation correction, spacing, and final visual alignment.
Household organization
Align clothes hangers
Two-handed manipulation of deformable garments and hangers inside a constrained wardrobe environment.
Food sorting
Arrange fruit
A short pick-and-place sequence showing object recognition, spatial grouping, and careful food handling.
Workspace reset
Clear desk trash
Desktop cleanup with visual search, collection of small waste items, and disposal into a nearby bin.
Cable management
Coil a charging cable
Bimanual handling of a flexible cable, including endpoint control, repeated coiling, and compact placement.
Facility cleaning
Erase a whiteboard
Broad surface wiping with tool contact, coverage planning, repeated passes, and completion inspection.
Fine manipulation
Flower arrangement
Delicate stem selection, insertion, rotation, and composition inside a vase using coordinated two-hand motion.
Multi-step assembly
Install a ping-pong net
Long-horizon assembly with component positioning, clamp adjustment, tensioning, and cross-table coordination.
Object interaction
Open a glasses case
Close-range manipulation of a hinged container, capturing grasp transitions, opening state, and object access.
Flexible object handling
Organize an earphone cable
A detailed untangling and coiling task requiring cable tracking, endpoint separation, and bimanual coordination.
Food preparation
Peel and prepare garlic cloves
Fine finger manipulation for separating cloves, removing skin, and preparing ingredients on a work surface.
Household placement
Return a bathroom stool
Whole-body object relocation through a narrow bathroom space with obstacle awareness and final placement.
Household storage
Put away a medicine box
Retrieval and cabinet storage with box handling, shelf localization, door interaction, and task completion.
Desktop tool maintenance
Refill staples in a stapler
Tool-state reasoning through opening a stapler, positioning a staple strip, closing, and checking readiness.
Device maintenance
Replace a remote-control battery
Battery replacement with cover removal, polarity-aware insertion, closure, and small-component handling.
Deformable material handling
Roll a felt sheet
Long-form bimanual manipulation of a flexible sheet, maintaining edge alignment and consistent roll tension.
Kitchen organization
Sort and store clean cups
Sorting clean drinkware by type and placing it into storage with grasp, stack, and collision awareness.
Object classification
Sort cutlery into a box
Repeated utensil pickup, visual classification, orientation, and placement into a divided organizer.
Controlled pouring
Sprinkle salt
Container opening, controlled dispensing, and closure capture a compact sequence with quantity-sensitive motion.
Kitchen storage
Store cups in a cabinet
Cabinet loading with repeated cup transfer, shelf placement, spacing decisions, and door interaction.
Object retrieval
Take milk from storage
A short retrieval task covering cabinet access, target selection, grasping, and environment state change.
Twist closure
Tighten a hand-cream cap
Two-handed tube stabilization and rotational cap tightening with small-object alignment and torque control.
Office tool operation
Top up stapler staples
A second stapler-loading sequence with tray access, refill alignment, closure, and functional handling.
Power interface handling
Unplug a charger
Cable tracing and plug removal from a constrained outlet area with controlled force and endpoint awareness.
Kitchen cleaning
Wash a fruit plate
Sink-side dish washing with water control, surface coverage, rinsing, and wet-object handling.
Large-surface cleaning
Wipe a ping-pong table
Extended whole-body cleaning over a large surface with route planning, reach, and coverage inspection.
Industrial workspace cleaning
Wipe a tool workbench
Long-horizon workbench cleaning around tools and obstacles with repeated wiping and workspace reset.
EgoHub Data Platform
A simple operating system for data delivery.
EGOHUB DATA PLATFORMBatch annotation console
operatortooltarget area
action: pickobject: cupstate: aligned
CollectAnnotateQADeliver
Collect
Translate the brief into field tasks, capture protocols, consent handling, and operational checks.
Annotate
Structure scenes, actions, objects, instructions, and review notes into model-usable formats.
QA
Review samples, flag ambiguity, verify completeness, and document delivery confidence.
Deliver
Package datasets, metadata, quality evidence, and handoff notes for downstream AI teams.
About Us
Data infrastructure built for Physical AI.
Global collection networkAI-driven pre-labelingHuman quality assurance
Egohub is a data infrastructure company built for Physical AI, providing high-quality, scalable real-world data for embodied AI, world models, and robotics research and development.
Powered by a globally distributed data collection network, an AI-driven automated pre-labeling platform, and a rigorous human quality assurance system, Egohub delivers end-to-end services spanning data requirements analysis, collection program design, data collection, annotation, quality control, and delivery.
Egohub currently operates across more than ten countries in Asia, Africa, and other regions, covering dozens of real-world environments, including homes, offices, retail spaces, manufacturing facilities, and agricultural settings. By providing diverse, authentic, and scalable real-world data, Egohub helps customers accelerate the training, evaluation, and deployment of Physical AI models.
HomesOfficesRetailManufacturingAgriculture
Kenya hub
Nairobi gives embodied AI teams a real-world operating base.
EgoHub AI is positioned in Kenya to support diverse field environments, multilingual operations, and managed teams that can translate physical-world tasks into dependable data workflows.
The result is a practical bridge between AI infrastructure and the hard-to-capture reality that robotics systems need to learn from.
Working with EgoHub AI
Clear inputs. Inspectable outputs.
What is embodied AI data?
Embodied AI data captures how people, objects, spaces, and tasks interact in the physical world. It can support robotics, world-model, multimodal, evaluation, and physical AI workflows.
What can an EgoHub data package include?
Depending on the brief, a package can include video assets, capture specifications, metadata, annotation files, QA notes, known-issue references, and a delivery manifest.
How is dataset quality reviewed?
Quality checks can cover capture integrity, protocol compliance, annotation coverage and consistency, issue tracking, and final delivery validation against the agreed brief.
What should a robotics data brief include?
Start with the target task, operating environment, required modalities and viewpoints, annotation schema, acceptance criteria, and the format your training or evaluation pipeline expects.
Start a data pipeline