Healthcare

AI-Powered Regulatory Intelligence with LLMs

Cut through complexity and stay ahead of compliance with RapidCanvas. Our LLM-powered solution can transform vast regulatory texts into actionable intelligence—helping pharma, healthcare, and food industries navigate legal frameworks with speed, precision, and confidence.

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Navigating Regulatory Complexity Is Time-Consuming and Risky

Highly regulated industries face a growing volume of evolving legal documentation—often dense, scattered across multiple sources, and written in complex language. Manual review and interpretation waste time and increase the risk of missing critical compliance changes.

Overwhelming Volume of Regulatory Texts

Teams struggle to keep up with thousands of pages of guidelines, updates, and regulatory publications.

Difficulty Interpreting Complex Legal Language

Regulations are often written in dense, technical language that requires expert interpretation.

Manual Research is Time-Intensive

Searching for relevant information across different agencies and regions slows down decision-making.

Risk of Non-Compliance

Missed updates or misunderstood requirements can lead to fines, recalls, or operational disruptions.

Limited Access to Subject Matter Experts

Regulatory specialists are scarce—and their time is often consumed by repetitive document review.

How RapidCanvas Uses LLMs to Automate Regulatory Analysis

At the heart of this solution is a Retrieval-Augmented Generation (RAG) framework, combining search and summarization capabilities of LLMs with a curated knowledge base built from health regulatory sources. Here’s how the process works:

Curated Data Collection

Ingest documents from regulatory bodies (FDA, EMA, WHO, etc.), guidance papers, compliance notices, and legal publications.

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Knowledge Indexing & Embedding

Process and index content using embeddings to make it searchable and context-aware for the LLM.

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Retrieval-Augmented Generation (RAG)

When a user submits a query, the system retrieves the most relevant document fragments and feeds them into the LLM for context-driven summarization or Q&A.

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Custom Query Handling

Natural language questions are enabled - about specific regulations, clauses, or cross-border compliance rules—providing accurate, traceable responses.

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