Real-world data collection
Managed field teams capture physical AI data from urban, indoor, mobility, retail, and human-task environments across Kenya.
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.
Egocentric task capture
Workplace procedures
Object identification
Video evaluation
Video quality control
Video classification
Smartphone egocentric capture
Pose evaluation
Video comparison A
Video comparison B
Prompted video review
Prompt benchmark capture
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 include clips, metadata, capture specs, consent notes, QA evidence, and schema documentation your team can inspect before training or evaluation.
MP4 clipsJSON metadataQA notesDelivery manifest
objecthand00:08.2 / task phase
delivery.jsonready
task_labelobject_identification
camera_viewegocentric + review
qa_statussample checked
handoffclips + metadata
Capabilities
From messy field reality to training-ready datasets.
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
Human-perspective data
Egocentric task capture
Evaluation signal
Pose evaluation
Operations setup
Field infrastructure
Service workflow
Barista workplace task
Instruction following
Prompted video review
Quality review
Video evaluation
Video cases
Concrete examples of data work in motion.
Human-perspective data
Egocentric task capture
First-person interaction footage for embodied agents that need to understand hands, objects, intent, and task context.
Real-world operations
Workplace procedures
Structured workplace scenes for training models on repeatable human workflows, service tasks, and environment changes.
Annotation workflow
Object identification
Object-level review loops that turn field video into clean labels, references, and model-ready metadata.
Quality review
Video evaluation
Evaluation pipelines for comparing video outputs, checking consistency, and documenting dataset quality signals.
QA sampling
Video quality control
Sampling and quality checks help teams catch ambiguity, motion issues, and incomplete task coverage before delivery.
Model feedback
Video classification
Classification-ready clips and review states support downstream training, benchmarking, and model feedback loops.
Mobile field capture
Smartphone egocentric capture
Phone-based first-person capture makes field data collection flexible across daily tasks, spaces, and operator workflows.
Evaluation signal
Pose evaluation
Pose-focused review helps validate human motion, body position, and action quality for embodied intelligence datasets.
Model evaluation
Video comparison A
Side-by-side evaluation material supports reviewer decisions when model outputs or captured clips need structured comparison.
Model evaluation
Video comparison B
Additional video evaluation examples expand reviewer coverage across motion, object state, and scene-level quality.
Instruction following
Prompted video review
Prompt-led review clips help align video observations with task instructions, model prompts, and expected behavior.
Instruction following
Prompt benchmark capture
Benchmark-style footage supports repeatable review tasks for instruction fidelity, action completion, and scene grounding.
Safety workflow
Red-team output review
Safety review footage helps stress-test model behavior, flag weak spots, and create auditable reviewer evidence.
Service workflow
Barista workplace task
Service environments create realistic sequences of object handling, human action, timing, and workspace constraints.
Operations setup
Field infrastructure
Infrastructure clips show how collection systems, environments, and capture setups support reliable data operations.
Visual annotation
Fashion attribute review
Fine-grained visual categories support object attributes, style labels, and appearance-based review workflows.
Synthetic interaction
Game capture evaluation
Interactive capture cases broaden evaluation coverage across screen-based actions, timing, and visual state changes.
Safety workflow
Generative safety review
Safety-oriented review tasks help identify problematic model behavior and structure reviewer feedback.
Benchmarking
Prompt benchmark operations
Prompt benchmarks connect reviewer judgments, video evidence, and consistent criteria for model comparison.
Safety workflow
Red-team scenario
Red-team clips add adversarial and edge-case coverage to quality review and safety evaluation pipelines.
Real-world capture
Extended field capture
Long-form capture expands coverage for physical-world context, continuity, and multi-step review scenarios.
Real-world capture
Extended environment sample
Additional field footage helps represent broader scene variety, operational setup, and data review complexity.
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.
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.
Start a data pipeline
Bring a robotics or physical AI data brief to EgoHub AI.
Contact hello@egohub.ai