AI Research Lab

Making AI Smarter, Safer,and More Efficient.

We're an AI research lab building the protocols, datasets, and efficient methods that power the next generation of intelligent applications. Most of our work is open.

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Our Mission

Building the protocols that power AI products

We're an AI research lab focused on the foundational work that makes intelligent applications possible. We design protocols, generate synthetic training data, study model development, and explore emerging AI fields. Most of our research is published openly — because the future of AI should be built together.

Protocol Research

Designing foundational protocols that enable AI-native applications — from structured UI representations to model context optimization.

OpenAll specs published

Synthetic Data Generation

Creating high-quality synthetic datasets that improve model performance. Better training data means smarter, more reliable AI systems.

10B+Tokens generated

Model Development

Researching efficient architectures, fine-tuning methods, and inference optimization. Making models smaller, faster, and more capable.

50xContext efficiency

Open Research

Most of our research is published openly. Papers, datasets, model weights, and protocols — available for the community to build upon.

100%Research public
Research Areas

Protocols, data, and models — the foundations of AI

Our research spans the full stack of AI development — from how models understand context to how agents interact with the world.

Context Protocols

How do we give AI models the right context without overwhelming them? We develop protocols that structure information for optimal model comprehension and reduced token usage.

  • Model Context Protocol
  • Structured UI Protocol
  • Context compression

Synthetic Data

Training data is the bottleneck. We generate diverse, high-quality synthetic datasets for code, UI, reasoning tasks, and domain-specific applications.

  • Code generation datasets
  • UI/UX training data
  • Reasoning benchmarks

Model Efficiency

Smaller models that perform like larger ones. We research quantization, distillation, and architectural innovations that reduce compute without sacrificing capability.

  • Efficient architectures
  • Quantization methods
  • Inference optimization

Agent Systems

How should AI agents communicate, plan, and execute? We study multi-agent coordination, tool use, and the protocols that make autonomous systems reliable.

  • Multi-agent coordination
  • Tool use patterns
  • Autonomous planning

Human-AI Interaction

The interface between humans and AI matters. We research how to make AI systems more interpretable, controllable, and aligned with user intent.

  • Interpretability
  • User intent modeling
  • Feedback loops

Applied Research

Theory meets practice. Our research directly informs the products we build — Nokuva, Tavoc, and Foltrac are testbeds for our protocols and methods.

  • Design intelligence
  • Code understanding
  • Infrastructure automation
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