World Models for Developers & Startups.
Hyle Labs democratises access to World Models. Get API access to the world's best predictive models today and ship what wasn't possible yesterday.
DRIVING INNOVATION AT
A New AI Paradigm
The future of AI is not about generating tokens or pixelsโit's about predicting representations of the world.
Generative AI
Predicts tokens or pixels. Creates outputs by generating sequences. Limited understanding of underlying structure and physical consistency.
Predictive AI
Predicts representations of the future. Learns structured latent models of how the world evolves. Enables deeper understanding and physical reasoning.
JEPA Principles
Joint Embedding Predictive Architectures learn by predicting representations in latent space rather than pixel space. This approach captures abstract structure and enables self-supervised learning from raw video data.
Core Technology
Everything you need to plug world model intelligence into your product
Predictive Embeddings
High-dimensional representations that capture temporal dynamics and future states of visual scenes.
World Models
Learned models of physical environments that predict how scenes evolve over time without explicit supervision.
Video Representation Learning
Self-supervised learning from raw video streams that extracts meaningful structure without labels.
V-JEPA2
V-JEPA 2 is the world's leading open video world model. We give you direct API access to its predictive embeddings โ no infrastructure to manage, no research team required.
Real-World Applications
What you can ship in days โ not years โ with Hyle Labs API access
Physics Reliability Score for AI Video
Generative video models frequently produce physically inconsistent scenesโimpossible object motion, violations of gravity, unrealistic interactions. Using V-JEPA2 embeddings, developers can build systems that measure the physical plausibility of video sequences.
Workflow
- Input video generated by a model
- Extract predictive embeddings
- Measure consistency across time
- Compute physics reliability score
Applications
- Detecting AI-generated video artifacts
- Validating simulation outputs
- Content authenticity tools
Predictive Perception for Robotics
Robots must understand how environments evolve. Traditional approaches require massive labeled datasets and months of training. With Hyle Labs API, developers can instantly tap into V-JEPA2 embeddings to give robots the ability to anticipate object movement and reason about physical interactions โ from day one.
Pipeline
- Robot camera stream
- V-JEPA2 inference
- Predictive embedding stream
- Downstream control policy
Applications
- Autonomous manipulation
- Warehouse robotics
- Drone navigation
World Models for Autonomous Driving
Autonomous vehicles require strong scene understanding. With a single API call, developers can extract V-JEPA2 predictive embeddings from driving footage to build systems that model scene dynamics, detect anomalies, and predict future states โ without training a single model from scratch.
Capabilities
- Predicting pedestrian motion
- Detecting dangerous driving situations
- Improving planning algorithms
- Scene dynamics modeling
Use Cases
- Enhanced autonomous navigation
- Safety-critical prediction
- Traffic behavior analysis
Developer API
One API call. Instant access to world model intelligence.
Upload Video
Send video or frame sequence
Call Endpoint
Invoke inference API
Receive Embeddings
Get latent vectors
Build Applications
Use in downstream tasks
POST /vjepa2/embed // Input { "video_sequence": "base64_encoded_video", "format": "mp4" } // Output { "embeddings": [array of latent vectors], "temporal_features": [temporal representations], "metadata": { "frames_processed": 120, "embedding_dimension": 768 } }
Embedding Interpretability
Unlike raw high-dimensional vectors, Hyle Labs provides tools to make embeddings interpretable: visualization, clustering, similarity analysis, and explainable latent dimensions.
Scientific Foundation
Built on cutting-edge research in predictive learning โ standing on the shoulders of the world's leading AI labs.
Self-Supervised Learning
Learning rich representations without manual labels by predicting structure in data. Models train on raw video, extracting semantic meaning purely from temporal coherence.
Energy-Based Modeling
Frameworks for learning by minimizing energy functions over latent representations. Joint embedding spaces that capture what matters โ not what's visible.
Predictive World Models
Capturing abstract temporal dynamics rather than surface-level pixel patterns. The machine learns what will happen, not just what is happening.
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
The foundational paper behind our core technology. V-JEPA 2 demonstrates how joint embedding predictive architectures achieve state-of-the-art performance on video understanding and physical reasoning tasks โ without pixel-level generation.
Read on arXivFundamental AI Research โ Where the Science Originates
Pioneering work by Yann LeCun and the Meta FAIR team on Joint Embedding Architectures defines the science behind V-JEPA 2. Hyle Labs packages this frontier research into simple APIs, so you can access it without a PhD or a research budget.
Explore Meta AI ResearchHyle Labs democratises access to frontier research from Yann LeCun and Meta FAIR โ putting state-of-the-art world model technology within reach of every developer and startup, not just large research labs.
Request Early Access
Get early API access and start building on top of world models today