Janus: A RAG-Powered Legal and Compliance Knowledge Infrastructure
Understanding foreign regulations is difficult—even for legal professionals. Legal systems vary widely, and real enforcement standards are often opaque.
AI tools worsen the problem: LLMs frequently produce hallucinated legal answers without grounding in actual law, making them unreliable for compliance decisions.
Enterprises lack a trusted, jurisdiction-aware source of truth for cross-border regulation.
What it is: A legal infrastructure that encodes laws and local expert annotations into a structured, machine-readable system.
How it works: Queries first trigger retrieval of jurisdiction-specific laws and expert commentary, then reasoning is performed on this grounded context—not the open internet.
This ensures outputs that are traceable, jurisdiction-aware, and hallucination-resistant.
Janus is evolving to translate law into machine-executable constraints, starting with on-chain financial activity.
It targets market integrity risks (e.g., wash trading, TVL inflation, token manipulation) by combining legal rules, expert input, and on-chain signals.
Outputs are verifiable PoMI reports, designed for regulators, law firms, and auditors.
Differentiation: Jurisdiction-aware overrides + on-chain native risk detection beyond traditional compliance.
Janus moves beyond standard vector search, adopting a deep architecture optimized for the strict accuracy, traceability, and jurisdictional constraints of the legal domain.
Normalizes heterogeneous legal texts and implicit expert knowledge into a unified, machine-interpretable schema, preserving dependencies to build a hybrid Legal Knowledge Graph (LKG).
Maximizes recall and precision while strictly adhering to legal constraints (jurisdiction, effective dates). Fuses semantic, keyword, and graph traversal modes.
Produces highly reliable, grounded, and auditable outputs using retrieved legal evidence. Enforces a "citation-first" reasoning logic to eliminate LLM hallucinations entirely.
Janus's hybrid data model combines vector-based semantic retrieval, graph-based logical traversal, and schema-based absolute filtering (e.g., specific jurisdictions), making traditional static legal databases obsolete.
Same real-world query. Three retrieval architectures. Drastically different outcomes. This is why knowledge infrastructure design is the true moat.
LLM has no domain knowledge base. It scrapes fragmented public sources — KOL blogs, news, social media — and summarises whatever surfaces.
A pre-built vector KB of raw legal clauses. At query time, the agent fetches the matching clause and assembles a JSON prompt before calling the analysis model.
Knowledge unit is a compound vector: Clause + Expert_Notes + Regulator_Attitude. Senior practitioners review & annotate every statute before indexing. Runtime retrieval pulls all three via graph traversal.
Janus is designed to eliminate hallucinations. Evaluated against curated multi-jurisdictional legal QA datasets, its performance far exceeds generic LLMs and standard RAG models across all critical dimensions.
Illustrative data reflecting the architectural objectives and benchmark targets of the Janus system.
