Maat: The Agentic Legal Research Assistant for Competition Protection

Researchers have developed Maat, a specialized AI agent designed to assist competition law experts with legal research by leveraging retrieval-augmented generation (RAG) and tool orchestration. Unlike general-purpose AI assistants, Maat addresses critical gaps in competition law analysis by providing reliable official citations, reducing hallucinations, and offering domain-specific expertise through iterative design with legal professionals.

Maat represents a meaningful advancement in applying large language models to specialized legal domains where accuracy and citation integrity are non-negotiable. Competition law research traditionally demands extensive case review and precedent analysis—work that general-purpose assistants like ChatGPT and Claude have struggled with due to their tendency to fabricate case references and lack domain-specific knowledge. The development of Maat, built through iterative collaboration with actual competition law experts, addresses a real pain point in professional legal practice.

The broader context reveals growing recognition that LLMs require domain-specific architectures rather than one-size-fits-all solutions. SaulLM-7B and LegalGPT demonstrated early attempts at legal specialization, but competition law presents unique challenges: it requires understanding of antitrust principles, merger frameworks, and nuanced precedent analysis. Maat's ReAct agent design—orchestrating multiple tools for different research tasks—exemplifies a more sophisticated approach than simple prompt engineering.

The practical implications for legal professionals are substantial. By grounding responses in official sources through RAG and providing inline citations, Maat reduces research time while maintaining evidentiary standards essential for legal work. The fallback to web search for coverage gaps and clarification prompts for ambiguous queries demonstrate thoughtful design around real user workflows. This approach validates the trajectory toward specialized AI tools in professional services.

Looking ahead, Maat's success could accelerate development of AI assistants tailored to other specialized fields—intellectual property, securities law, and regulatory compliance. The availability of the dataset on GitHub creates opportunities for further refinement and potential commercialization. The key question remains whether similar architectures can achieve comparable performance improvements across other high-stakes professional domains.

• →Maat is a specialized AI agent designed specifically for competition law research that outperforms general-purpose assistants by grounding findings in official sources and reducing hallucinations.
• →The agent uses retrieval-augmented generation (RAG) and tool orchestration to provide reliable citations and falls back to web search when database coverage is insufficient.
• →Development was iterative and collaborative with actual competition law experts, ensuring the tool addresses real workflows and professional standards.
• →The approach demonstrates that domain-specific AI architectures outperform generic LLMs for specialized professional tasks requiring accuracy and citation integrity.
• →The publicly available dataset enables further refinement and could accelerate similar specialized AI tools in other professional legal and regulatory domains.