CarePilot AI

Inspiration

Healthcare professionals spend a significant amount of time on administrative tasks such as reviewing patient records, processing insurance documents, verifying compliance, and searching through medical guidelines. These repetitive workflows reduce the time available for direct patient care.

CarePilot AI was created to streamline these processes by combining AI-powered document understanding, semantic search, and intelligent workflow automation into a single assistant that supports healthcare teams.

What it does

CarePilot AI is an AI-powered healthcare copilot designed to assist medical staff with clinical documentation, patient record analysis, and administrative workflows.

Users can upload medical records, prescriptions, laboratory reports, insurance claims, and clinical guidelines. The platform automatically extracts key information, summarizes lengthy documents, answers questions using Retrieval-Augmented Generation (RAG), and generates structured reports with source citations.

The system also helps healthcare providers verify document completeness, detect missing information, and automate repetitive administrative tasks while keeping clinicians in full control of medical decisions.

Key Features

AI-powered medical document summarization
Patient record semantic search
Clinical knowledge assistant
Insurance and claims document analysis
Medical compliance checklist generation
Intelligent report generation
Multi-agent workflow automation
Human approval before sensitive actions

How we built it

The frontend was developed using Next.js, React, and Tailwind CSS to provide a fast and intuitive interface for healthcare professionals.

The backend uses Node.js with Supabase for authentication, secure storage, and PostgreSQL database management.

Medical documents are converted into vector embeddings and indexed for semantic retrieval. When users submit a question, the platform retrieves the most relevant clinical information before sending context to the language model, improving accuracy and reducing hallucinations.

Multiple AI agents coordinate document parsing, retrieval, summarization, validation, and report generation. LangGraph orchestrates these agents while Langfuse monitors prompts, responses, and performance.

Challenges we ran into

Healthcare data requires high accuracy and explainability. One challenge was designing an AI workflow that provides useful recommendations while clearly separating AI assistance from clinical decision-making.

Another challenge was handling diverse document formats, including scanned PDFs, handwritten notes, insurance forms, and laboratory reports.

Accomplishments that we're proud of

Built an AI healthcare copilot capable of understanding complex medical documents.
Implemented Retrieval-Augmented Generation to improve response reliability.
Designed a modular multi-agent architecture that can support different healthcare workflows.
Created an interface that simplifies document analysis while maintaining human oversight.

What we learned

We learned that healthcare AI is most effective when it augments professionals instead of replacing them. Reliable retrieval, transparent citations, and human validation are essential for building trustworthy AI systems in clinical environments.