MedevAI: Agentic Medical Device Regulatory Pathway Planner

Streamline FDA regulatory pathway planning with AI-powered predicate search and compliance automation—reducing 510(k) preparation time from weeks to hours.

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Inspiration

Bringing a medical device to market is fraught with regulatory hurdles, where even a single misstep contributes to a 15% submission failure rate. We developed MedevAI after witnessing startups lose precious time and resources on the manual, error-prone search for predicate devices, reference guidance, and the setup of practical device tests and clinical trials. These bottlenecks aren’t caused by a lack of data, but by the lack of accessible tools to integrate it; the FDA’s open data is a treasure trove that remains locked for most. MedevAI is the key. It was created from the conviction that intelligent automation can transform this high-risk, multi-day ordeal into a streamlined, reliable process, empowering innovators to focus on what they do best: building the future of medical devices—while agentic AI clears the path.

What it does

MedevAI is an intelligent regulatory assistant that transforms the complex FDA medical device approval process into a streamlined, AI-powered workflow. The platform serves as a comprehensive regulatory pathway planner that helps medical device companies navigate the intricate 510(k) submission process with unprecedented speed and accuracy.

Core Capabilities:

  • Intelligent Device Classification: Automatically determines FDA device class (I, II, III) and identifies appropriate product codes based on device description and intended use
  • AI-Powered Predicate Search: Performs semantic analysis to find the most suitable predicate devices from the FDA database, ranking them by substantial equivalence potential
  • Comparative Analysis Engine: Generates detailed side-by-side comparisons between user devices and potential predicates, highlighting similarities and differences that impact regulatory strategy
  • Regulatory Guidance Mapping: Automatically identifies and retrieves relevant FDA guidance documents, special controls, and testing requirements specific to each device type
  • Compliance Checklist Generation: Creates customized 510(k) submission checklists based on device classification and predicate analysis
  • Audit Trail Management: Maintains comprehensive, exportable audit logs of all AI decisions and reasoning for regulatory compliance

The system operates through an intuitive conversational interface powered by advanced AI agents, allowing regulatory professionals to interact naturally while maintaining the rigor and documentation standards required for FDA submissions.

How We Built it

The system's architecture is centered around a powerful AI agent designed to act as a specialized regulatory assistant. This agent is the core of the platform, automating and augmenting the complex work of regulatory professionals.

The frontend is a modern web application built with Next.js, React, and TypeScript, leveraging the App Router for a seamless user experience. The interface is designed with Shadcn UI for consistency and accessibility, styled with Tailwind CSS for responsive layouts, and enhanced with CopilotKit to deliver an intuitive, AI-powered conversational interface. Authentication is securely managed by NextAuth.js using Google OAuth 2.0.

The backend services are built on the high-performance FastAPI framework in Python. The system adopts an agent-based architecture using LangGraph to create state-managed, auditable workflows for regulatory tasks. Data is temporarily stored in a SQLite database via the SQLAlchemy ORM, with Redis integrated for intelligent caching and session management to maintain high performance.

Challenges we ran into

  • FDA API Complexity and Rate Limiting: The openFDA API has strict rate limits and inconsistent data formats. We implemented intelligent caching with Redis, circuit breaker patterns for resilience, and exponential backoff logic to manage these constraints effectively.
  • Regulatory Compliance Requirements: To meet the need for auditability and human oversight, We designed the system to produce complete reasoning traces for all AI decisions, confidence scores with detailed justifications, and exportable audit logs.
  • Complex State Management: Regulatory processes are long-running and can be interrupted. We used the LangGraph framework to build a state-based agent architecture with checkpoints, allowing workflows to be paused and resumed seamlessly.
  • Testing Reliability: Achieving consistent test results required creating mock FDA API responses, isolating database transactions for each test, and implementing performance monitoring with automated alerts.
  • User Experience for Domain Experts: We designed a dual interface that provides both a conversational AI for quick analysis and structured data tables with PDF export for formal submission work.

Accomplishments that we're proud of

Revolutionary Time Reduction: Successfully reduced predicate device identification from 2-3 days of manual research to under 2 hours of AI-assisted analysis, representing a 90%+ time savings for regulatory teams.

Regulatory-Grade AI Architecture: Built the first LangGraph-based agent system specifically designed for FDA compliance, featuring complete audit trails, confidence scoring, and human-in-the-loop validation that meets regulatory inspection standards.

Intelligent Semantic Search: Developed sophisticated predicate matching algorithms that go beyond keyword searches to perform semantic analysis of device descriptions and intended use statements, dramatically improving match quality and relevance.

Real-Time FDA Integration: Successfully integrated with the openFDA API to provide live, up-to-date regulatory data while implementing robust rate limiting, caching, and error handling to ensure system reliability.

Domain-Specific Expertise: Created an AI system that demonstrates deep understanding of FDA regulatory concepts like substantial equivalence, product codes, and CFR sections - essentially encoding regulatory expertise into software.

Comprehensive Testing Framework: Achieved high test coverage across frontend, backend, and integration layers, with specialized testing for regulatory workflows and FDA API interactions.

User-Centric Design: Delivered a dual-interface system that provides both conversational AI for quick analysis and structured data exports for formal regulatory submissions, perfectly balancing ease of use with professional requirements.

What we learned

Building MedevAI taught us several key lessons about developing AI-powered regulatory tools. On the technical side, we learned that an agent-based design using LangGraph can effectively manage complex, multi-step regulatory workflows in collaboration with humans. Integrating with the real-time openFDA API underscored the importance of robust engineering practices, including rate limiting, circuit breaker patterns, and comprehensive error handling. Most importantly, working in a regulated field requires a compliance-first mindset, which means embedding extensive audit trails, confidence scoring, and human-in-the-loop validation at every step.

From a domain perspective, we gained a deep appreciation for the nuances of regulatory work—designing and maintaining medical devices is hard work. From understanding FDA product codes and substantial equivalence criteria to handling the variable quality of public data, the challenges are significant. This often required creating sophisticated paperwork involving patients. We also learned that the user experience for regulatory professionals must blend conversational AI for ease of use with structured data exports for formal submissions. Such an approach can significantly reduce the workload of regulatory affairs teams and enable device companies to make faster, more informed decisions.

What's next for MedevAI

Enhanced AI Capabilities: Expanding the agent system to handle more complex regulatory scenarios, including De Novo pathway analysis, PMA submissions, and post-market surveillance requirements.

Global Regulatory Expansion: Extending beyond FDA to support EU MDR, Health Canada, and other international regulatory frameworks, creating a truly global regulatory intelligence platform.

Advanced Analytics Dashboard: Developing predictive analytics to forecast submission success rates, identify potential regulatory risks, and provide strategic recommendations based on historical FDA decision patterns.

Integration Ecosystem: Building APIs and integrations with popular QMS systems, PLM platforms, and regulatory databases to create a seamless regulatory workflow ecosystem.

Collaborative Features: Adding multi-user support, team collaboration tools, and version control for regulatory documents to support larger regulatory teams and cross-functional projects.

Machine Learning Enhancement: Implementing continuous learning capabilities that improve predicate matching accuracy and regulatory insights based on user feedback and FDA decision outcomes.

Mobile and Offline Capabilities: Developing mobile applications and offline functionality to support regulatory professionals working in various environments and connectivity conditions.

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