Simulation promises. We close the gap.
We build the training data that closes the sim-to-real gap.
Frontier
AI Lab
Leading
Smartphone Maker
Major
Social Platform
Top GenAI
Assistant Maker
Leading
Search Engine
Leading Global
Ecommerce
Frontier
Robotics Studio
Iconic
Smart Eyewear
World-Leading
Creative AI Platform
Premium
Automotive AI
Top-20
US Mortgage Lender
Major
Crypto Trading Platform
Global
Banking-as-a-Service
Emerging
BNPL Fintech
Global
Card Issuer
Tier-1
FinCrime Portfolio
Market Forces
Physical AI is scaling. The data gap is scaling faster.
Three numbers from the frontier of embodied AI—and what they demand from training data programs.
The sim-to-real gap explained in one number
12%
average success rate for state-of-the-art robots completing real household tasks in 2025 — tasks humans accomplish without thought. The gap between simulation and physical world remains the central unsolved problem in embodied AI.
Stanford AI Index 2026
Autonomous vehicles are scaling fast
450,000
weekly fully autonomous rides delivered by Waymo in 2025 — a 5X increase from 2024. The pace of commercialization is outrunning the speed at which most OEMs can build training data programs.
Stanford AI Index 2026
Driverless adoption is accelerating
175%
year-on-year growth in fully driverless rides on Apollo Go — the steepest single-year acceleration in commercially operated autonomous fleet history.
Stanford AI Index 2026
Proof in production
What Physical AI data programs actually delivered
Capture programs that closed the sim-to-real gap on real robotics hardware.
>99%
accuracy across driver safety dashcam data. Driver safety and dashcam AI program for a dashcam and driver safety platform.
50,000+
participants in wearable gesture dataset. Gesture intelligence dataset for wearable glasses for a wearable tech company.
700+
headcount deployed for 3D map & AR data. Large-scale 3D map and AR data program for 3D Map & AR Platform.
Across the GenAI lifecycle
Physical AI data that bridges the sim-to-real gap
Physical AI models fail at 12% success on simple household tasks—not because the algorithms are wrong, but because the training data is wrong. Physical AI training data is not a labeling problem. It is a physics problem.
phase 01
Egocentric and embodied capture
Firstsource builds egocentric datasets from the robot's or wearable's point of view—not from fixed cameras or simulated environments. Capture programs deploy participants at scale across real environments.
50,000 hours · Robotics Studio VLA
We delivered 50,000 hours of accepted egocentric video for dexterous-manipulation VLA training.
Egocentric capture · Robotics Studio VLA
Phase 02
3D annotation and spatial labeling
Robots and AR systems must understand the world in three dimensions. Firstsource annotates LiDAR, depth camera, and point cloud data with 3D bounding boxes, semantic segmentation, and spatial relationship labeling.
3D Map & AR Platform
We deployed 700+ specialists for a 3D-map and AR data program.
3D annotation · 3D Map & AR Platform
phase 03
Gesture and motion capture
Spatial computing, AR, and wearable AI systems must interpret human gesture reliably. Firstsource runs large-scale gesture capture programs with 50,000+ participants, diverse demographics, controlled and uncontrolled settings.
Wearable Glasses
We recruited 50,000+ participants for a wearable-glasses gesture dataset with demographic diversity.
Gesture capture · Leading Wearable Tech Company
Phase 04
Sensor fusion and multimodal integration
Physical AI systems combine cameras, LiDAR, radar, IMU, GPS, and tactile sensors. Firstsource aligns, synchronises, and annotates multi-sensor streams to match the model's actual input architecture.
Driver Safety
We reached >99% accuracy on a driver-safety dashcam program for multimodal driver monitoring.
Sensor fusion · Global dashcam and driver safety platform
Phase 05
Sim-to-Real pipeline support
Simulated training data has a ceiling. Firstsource builds and validates sim-to-real bridge datasets: real-world capture programs designed to expose and close the gaps that simulation leaves.
Spatial VR Headset
We hit 94.7% top-1 gesture accuracy, demonstrating successful sim-to-real transfer.
Sim-to-real · Next-Gen Spatial VR Headset Maker
Program spotlight
One program. 50,000 hours. Teaching robots what their hands are touching.
50,000 hours of contact-rich manipulation data—teaching robots what their hands are touching.
Client Program
50,000 hours. Egocentric data. Dexterous manipulation AI.
A robotics studio building vision-language-action models for dexterous manipulation needed egocentric training data at scale—video, tactile, and proprioceptive streams captured from the robot's point of view across real-world environments.
Firstsource built a specialist capture workforce and delivered 50,000 hours of accepted egocentric video data, structured to VLA model specification with tactile and proprioceptive data capture integrated alongside the visual feed. Multi-stage QA pipeline ensured acceptance rate above program threshold.
Firstsource built a specialist capture workforce and delivered 50,000 hours of accepted egocentric video data, structured to VLA model specification with tactile and proprioceptive data capture integrated alongside the visual feed. Multi-stage QA pipeline ensured acceptance rate above program threshold.
Program dimensions
50K
hours of egocentric data
VLA Spec
model-specification structured
Multi-sensor
visual + tactile + proprioceptive
QA Pipeline
above-threshold acceptance rate
Where it applies
Where embodied-AI data meets the physical world
What contact-rich, sensor-grounded capture changes for each sector’s systems.
AI labs and foundational models
Embodied-AI research teams get contact-rich, sensor-grounded trajectories that let world models learn physics, not just pixels.
Learn more
Technology
Spatial-computing and wearable programs get ego-exo capture across real environments, devices, and users.
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Robotics
Manipulation and navigation models get teleoperation and motion data that closes the sim-to-real gap on real hardware.
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Banking and financial services
Branch-automation and document-handling robotics get capture programs scoped to controlled, compliant environments.
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How we deliver
How we run a physical AI data program
How a capture program goes from participant recruitment to validated, model-ready data.
Participants
large
participant pools across 100+ countries
Physical AI programs need participants, not annotators—people wearing headsets, manipulating objects, and driving vehicles, matched by demographic profile, physical ability, and environment type.
Activation
48hrs
to program activation
We activate programs in under 48 hours via the Gigsourcing Platform, and we deploy 50,000+ participants in a single gesture-dataset program.
Quality Assurance
35+
KPIs monitored per capture
We run multi-stage sensor-data QA for frame dropout, synchronisation errors, and calibration drift, with annotation-consistency IAA scoring across 3D bounding-box and segmentation tasks.
INSIGHTS
Latest from the Firstsource team
Insights from the field, real operations, real outcomes, and perspectives from the people making it work in live operations.
Start a conversation
Talk to a physical AI lead
Tell us what you're building. We'll scope the capture program.
- Capture modality and environment
- Participant demographics and scale
- Sensor fusion requirements
- Sim-to-real pipeline integration
- Quality thresholds and delivery timeline