Beyond Simple Retrieval
Standard RAG (Retrieval-Augmented Generation) pipelines are fundamentally limited. They slice documents into arbitrary chunks and rely on simple cosine similarity to find "relevant" text. For complex scientific literature or highly technical market research, this semantic flattening destroys critical nuance.
NARA leverages the Yudi AI CMP infrastructure to process dense documents experientially. It doesn't just retrieve chunks; it builds a relational map of the concepts within the text, allowing it to synthesize multi-document hypotheses with a level of rigor that standard LLMs cannot achieve.
Core Workflows
Literature Synthesis
Upload dozens of PDFs. NARA maps the conceptual overlap, identifies conflicting data points across papers, and generates a unified structural review.
Hypothesis Generation
By relying on sparse relational binding, NARA can identify non-obvious connections between disparate fields of research that traditional vector search misses.
Persistent Project Memory
Unlike chat windows that forget context, NARA maintains a persistent state for your specific research project, continuously learning as you feed it new data over months.
Citation Grounding
Every claim NARA makes is bi-directionally linked to the exact structural node in the source document, eliminating hallucination in critical research workflows.
Roadmap & Access
NARA is currently in private beta with select academic partners and enterprise research teams. We are gradually expanding access to ensure the architecture scales efficiently with our infrastructure.