Does Your AI Video Break the Laws of Physics?
Plug our Physics Consistency Score (PCS) into your video pipeline and catch physical hallucinations before they reach production — get a 0–100 score measuring whether your AI-generated video respects gravity, inertia, and object interactions.
DRIVING INNOVATION AT
Physical Consistency Score (PCS)
Our core product. Ensure your generated videos obey the laws of physics seamlessly, directly through our API.
EASY API INTEGRATION
The Physical Consistency Score (PCS) evaluates the physical plausibility of video sequences in real-time. Without maintaining any complex infrastructure, you can integrate PCS directly into your workflow to instantly filter out generated videos with impossible object motion or physics violations.
A New AI Paradigm
The future of AI is not about generating tokens or pixels—it'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.
Self-Supervised Learning (SSL)
Our models learn by observing massive amounts of raw video data without expensive human labeling. By predicting abstract hidden representations instead of individual pixels, the system builds a robust, structured understanding of how the physical world truly operates.
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.
Real-World Applications
What you can ship in days — not years — with Hyle Labs API access
Physics Reliability Score for AI Video
Generative video models frequently produce physically inconsistent scenes—impossible object motion, violations of gravity, unrealistic interactions. Using our predictive 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 our predictive embeddings to give robots the ability to anticipate object movement and reason about physical interactions — from day one.
Pipeline
- Robot camera stream
- Physics-based 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 predictive embeddings from driving footage to build systems that model scene dynamics, detect anomalies, and predict future states — 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 /pcs/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 — 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 — 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.
Self-Supervised Video Models for Physical Reasoning
The foundational framework behind our core technology. Joint embedding predictive architectures achieve state-of-the-art performance on video understanding and physical reasoning tasks — purely by learning latent energy-based models.
Read Architecture OverviewTranslating Research into Production APIs
Pioneering work on self-supervised representations defines the science behind our engines. Hyle Labs packages this frontier academic research into simple, scalable APIs, so you can access physical intelligence without a PhD or an R&D budget.
Explore API FeaturesHyle Labs democratises access to cutting-edge physics AI and self-supervised learning — putting state-of-the-art spatial intelligence 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