Scale your AI models with accurate, human-in-the-loop annotation across images, video, LiDAR & multimodal data.
The Challenge
Training robots on clean, well-labeled real-world data is one of the hardest bottlenecks in Physical AI development. Here's what most teams run into:
Real-world environments are unpredictable. Getting enough diverse, high-quality data takes time that most teams don't have.
Fieldwork, hardware, logistics, and labor add up fast when you're doing it yourself.
Models fail at edge cases. Capturing rare, critical scenarios requires deliberate planning, which most workflows skip.
Combining LiDAR, RGB, and depth data from multiple sensors is technically complex and error-prone.
Labeling multimodal data at scale demands domain knowledge, not just manual labor.
What We Do
We handle the full pipeline, from planning what data to collect to delivering datasets that are ready to train on.
We work with your team to understand the robot's use case, environment, and edge cases before a single sensor goes live.
Our field teams collect data across real environments using LiDAR, RGB cameras, depth sensors, and other hardware relevant to your use case.
Every dataset goes through structured labeling and multi-layer quality checks. No shortcuts.
Clean, formatted, and structured datasets delivered to your pipeline. No reformatting, no back-and-forth.
Where We Operate
Navigating shelves, avoiding workers, picking objects. We collect the data that helps warehouse robots handle real floor conditions.
Indoor and outdoor environments, varied lighting, and obstacle-heavy spaces. We cover the scenarios that simulators miss.
Crop rows, terrain variation, and weather conditions. Ground-truth data for robots working where infrastructure is minimal.
Heavy equipment, complex workspaces, safety-critical zones. Data collected and annotated to reflect real factory and site conditions.
Multimodal Coverage
Physical AI models learn from more than one signal. We collect and organize multimodal datasets that reflect real-world perception across environments and tasks.
High-volume visual data capture across controlled and real-world conditions.
Structured collection for depth-aware systems and spatial understanding.
Useful for context-rich environments where sound and ambient inputs matter.
Aligned and synchronized datasets across multiple input sources for better downstream training.
Trust & Compliance
We follow secure handling practices, controlled storage and transfer methods, and NDA-led engagement models that support enterprise requirements.
Why Gamasome
We don't just label data. We understand what the data needs to teach your robot, and we build the collection process around that.
| Activity | Gamasome | Typical Vendors |
|---|---|---|
| Turnaround | 2 to 6 weeks | 8 to 12 weeks |
| Workforce scalability | On-demand, scalable | Fixed capacity |
| Domain expertise | Physical AI specialists | General annotation |
| Pipeline ownership | End-to-end | Fragmented |
| Communication | Direct, no middlemen | Account manager layers |
Process
Every project is shaped around your model goals, operating environment, and deployment needs.
We align on your robot use case, target environment, sensor stack, and output format.
We map the real-world conditions, movements, interactions, and exceptions your model needs to learn from.
Our team captures data across the agreed scenarios using the right hardware, operators, and protocols.
We annotate the collected data using task-specific workflows built for computer vision and Physical AI pipelines.
Every dataset goes through structured QA to improve consistency, reduce noise, and catch missed cases.
We package and deliver the data in a format your team can plug into training, testing, or validation workflows.
From scenario planning to multimodal collection, annotation, and QA, Gamasome helps Physical AI teams get the real-world data they need to train with more confidence and less operational friction.