At Frontier AI, we create some of the world’s highest-quality video datasets for training advanced robotics and frontier-scale AI systems. Our labelling process is designed from the ground up for long-form, real-world video captured from the human point of view — the same conditions where tomorrow’s robots will operate.
To ensure reliability, safety, and performance, every frame goes through a rigorous, multi-stage annotation and quality assurance pipeline built specifically for robotics and risk-aware AI.
Robots learn from experience. Our datasets provide that experience at scale.
We collect video across homes, workplaces, hospitals, factories, warehouses, and commercial environments. Each scene is carefully annotated to map:
This structured, multi-layer approach gives AI systems the exact information required to navigate and act safely in the real world.
Our labelling framework is built specifically for robotics and risk modelling.
Each video is annotated across three layers:
We label the full spectrum of real-world objects — furniture, tools, appliances, cables, surfaces, openings, spillages, edges, and more — using bounding boxes, masks, and spatial annotations designed for robotic perception.
Robots must understand not just what an object is, but what state it is in.
Our annotators tag conditions such as:
This produces “risk-aware” datasets that enhance the safety and robustness of robotic decision-making.
Each environment is categorised to help AI models understand domain-specific patterns — for example:
This makes our datasets immediately useful for OEMs, insurers, autonomy stacks, and simulation engines.
High-quality labels are essential for high-performing AI.
We use a multi-layer QA pipeline to ensure consistent, accurate, trustworthy annotations.
Our annotators are trained specifically in:
Every labeler completes domain-specific onboarding before they touch production data.
Every video passes through:
We measure accuracy continuously and provide feedback loops to maintain stable performance across thousands of hours of video.
We audit a rotating sample of each batch to measure:
This creates a predictable, measurable quality profile for every dataset.
For enterprise customers, we incorporate an additional review layer where OEMs or robotics partners can evaluate samples and request refinements.
This ensures that every dataset is tuned to the specific needs of the model being trained.
Frontier AI uses a “model-in-the-loop” workflow to enhance speed and precision.
Our internal models (trained on our proprietary dataset) pre-annotate video sequences. Human annotators then refine and validate the results, ensuring:
We use active learning systems to automatically identify:
This ensures Frontier AI prioritises the most valuable frames, improving dataset quality while reducing cost.
Frontier AI handles real-world video with the highest level of responsibility.
We maintain an audit trail for every labelled dataset we deliver.
Robotics companies work with Frontier AI for three key reasons:
Our datasets expose models to clutter, unpredictability, hazards, and the messy reality robots must master.
Industry-first tagging of hazards, conditions, and environment states helps robots make safer decisions.
Every dataset goes through our structured QA pipeline, combining expert human judgement with advanced AI assistance.
As a result, our training data consistently improves navigation reliability, manipulation accuracy, and real-world performance for frontier-scale AI models.