It's like having a regulatory affairs expert who has memorized every FDA document ever published and can instantly show you the exact page where they found the answer.

Rhizome AI's core product use case is a transformer-based LLM chatbot that uses retrieval-augmented generation to search across 1,000+ regulatory documents from 30+ datasets (FDA guidance documents, 510(k) databases, drug approval letters, warning letters, inspection databases, EMA documents, and ClinicalTrials.gov). When a user asks a regulatory question, the system performs dense vector search across its indexed corpus, retrieves the most relevant document passages, and feeds them into a fine-tuned language model that generates a comprehensive answer. A proprietary validation layer then verifies that every claim in the response is directly supported by a cited source at the paragraph level, eliminating hallucinations. The system refreshes data weekly to ensure currency with the latest regulatory actions. This replaces the traditional workflow of regulatory affairs professionals manually searching FDA.gov, reading hundreds of pages of guidance documents, and cross-referencing multiple databases—a process that typically takes days to weeks per complex query.

It's like replacing a library research assistant who takes two weeks to find and photocopy the right pages with a photographic-memory savant who highlights the exact sentence you need before you finish asking the question.

It's like connecting the dots between every FDA warning letter, device recall, and drug approval to spot trouble before it finds you.

Rhizome AI employs Apache AGE graph databases to model complex relationships between regulatory entities—drugs, medical devices, companies, active ingredients, therapeutic areas, FDA divisions, inspection findings, warning letters, 510(k) clearances, approval pathways, and adverse event reports. By structuring these relationships as a knowledge graph rather than flat document storage, the system can traverse multi-hop connections that would be invisible in traditional keyword search. For example, it can identify that a specific manufacturing facility cited in a recent FDA warning letter also produces components for three other approved devices, or that a particular regulatory reviewer's historical approval patterns correlate with specific submission strategies. Named Entity Recognition (NER) models automatically extract and classify regulatory entities from unstructured documents during ingestion, while entity resolution algorithms deduplicate and link mentions across different data sources (e.g., matching a company name in a warning letter to its 510(k) submissions). This graph intelligence layer powers both the chatbot's contextual understanding and emerging analytical features that help regulatory teams anticipate enforcement trends and optimize submission strategies.

It's like having a conspiracy theorist's wall of red string connecting photos and newspaper clippings—except it's actually accurate, covers every FDA document ever filed, and the connections are backed by real data instead of paranoia.

It's like having a tireless intern who reads every new FDA document the moment it's published, perfectly organizes it, and files it exactly where your AI can find it.

Rhizome AI's engineering team has built a fully automated data pipeline that continuously ingests, cleans, normalizes, classifies, and indexes regulatory documents from 30+ heterogeneous data sources including FDA.gov (guidance documents, warning letters, 510(k) summaries, drug approval packages, inspection databases, advisory committee transcripts), EMA databases, ClinicalTrials.gov, and other global health authorities. The pipeline handles diverse document formats (PDFs, HTML pages, structured databases, XML feeds) and applies ML-driven document classification to automatically categorize each document by type, therapeutic area, device class, regulatory pathway, and jurisdiction. Text extraction models handle complex layouts including multi-column PDFs, tables, headers/footers, and embedded images. Deduplication algorithms identify and merge overlapping or updated versions of documents, maintaining a single source of truth. The normalization layer standardizes terminology across sources (e.g., mapping different naming conventions for the same drug or device across FDA and EMA databases). Once processed, documents are chunked at optimal granularity, embedded using dense vector models, and indexed in both the vector store and graph database for retrieval. The entire pipeline runs on weekly refresh cycles, ensuring the knowledge base stays current with the latest regulatory actions—a critical requirement in an industry where a single missed guidance update can derail a submission timeline.

It's like a Roomba for regulatory paperwork—it quietly runs in the background, sucks up every new FDA document from every corner of the internet, sorts them into perfectly labeled folders, and never once complains about reading a 400-page guidance document at 3 AM.