NumericalArt is an early-stage AI research startup developing foundational tools for autonomous agent communication. Through our core project, "Agents For Agents", we explore how Large Language Models (LLMs) and AI agents can structure data, communicate internally, and collaborate across tasks. We focus on designing specialized LLM's "syntactic sugar", internal agent languages, and algorithmic data preparation strategies to enhance Retrieval-Augmented Generation (RAG), enable more effective agent interoperability and improve AI-native search.

Our approach involves experimenting with innovative methods to simplify and optimize communication between AI agents. We actively utilize modern mathematical techniques and algorithmic insights to refine data structures and interaction patterns. NumericalArt is committed to creating practical, effective methods and tools that support the evolution and improvement of agentic AI systems.

Token-saving and semantic-clearence for agentic AI

Our OverLang Codec enables dramatic efficiency gains in AI agent communication through reinforcement learning-trained compression that maintains lossless decoding across any LLM.

AI-Enhanced Document Processing

Our Universal Document Processor transforms any document format into AI-ready structured data through advanced computer vision and intelligent content analysis, creating the foundation for reliable knowledge extraction and agent communication workflows.

Enterprise-Scale RAG with Multi-Agent Intelligence

Our Advanced Multi-Agent RAG System coordinates specialized agents to deliver enterprise-grade retrieval with fact-checking and verification, combining traditional search methods with modern vector approaches for accurate, traceable responses.

Vision: Agent-to-Agent Internet

Our Agents for Agents Platform creates direct agent-to-agent communication channels that bypass traditional web scraping, enabling semantic knowledge exchange through compressed protocols and structured endpoints designed specifically for autonomous AI systems.