-
-
AI-generated healthcare diligence report with structured operational and regulatory intelligence outputs.
-
Automated extraction of reimbursement, operational, and regulatory risks from healthcare SEC filings.
-
Agentic retrieval and orchestration workflow powering SEC filing analysis and structured diligence generation.
Inspiration
MedIntel AI was inspired by how fragmented and time-consuming healthcare diligence workflows can be. Analysts often spend hours reviewing SEC filings, identifying reimbursement and regulatory risks, organizing operational insights, and synthesizing large amounts of unstructured information into something actionable.
I wanted to explore how agentic AI workflows and retrieval systems could help automate parts of that process while still keeping humans in control of decision-making. Since healthcare is such a regulation-heavy and operationally complex industry, it felt like the ideal environment to test enterprise AI orchestration and document intelligence workflows.
What it does
MedIntel AI is an agentic healthcare intelligence platform designed to streamline diligence and risk analysis workflows for healthcare companies.
The platform can:
- Retrieve and analyze SEC filings
- Process large volumes of healthcare regulatory disclosures
- Extract operational and reimbursement-related risks
- Generate AI-powered diligence summaries and structured outputs
- Support contextual Q&A using retrieval-augmented generation (RAG)
- Organize information into a more streamlined agentic workflow
The goal was to build something that feels closer to an enterprise intelligence pipeline than a simple chatbot.
How we built it
The project was built using:
- Python
- Streamlit
- Ollama and Llama 3.2
- OpenAI API
- Retrieval-Augmented Generation (RAG)
- SEC EDGAR data retrieval
- Vector embeddings and semantic search
- Pandas and financial data APIs
The workflow begins by retrieving publicly available SEC filings and disclosures. Documents are then chunked, embedded, and indexed into a semantic retrieval pipeline capable of surfacing contextually relevant information during analysis.
From there, LLM orchestration workflows synthesize information, summarize risks, and generate structured diligence outputs.
Conceptually, the architecture looks like:
User Input → SEC Filing Retrieval → Document Chunking → Vector Embeddings → Semantic Search → LLM Analysis → Risk Extraction → Structured Diligence Output
A major focus throughout development was building a workflow that felt enterprise-oriented and agentic rather than simply generating isolated model responses.
Challenges we ran into
One of the biggest challenges was working with the complexity and inconsistency of healthcare filings. SEC documents are long, dense, and highly technical, which made retrieval quality and context management difficult.
Another challenge was balancing retrieval precision with model context limitations. A significant amount of iteration went into improving chunking strategies, semantic retrieval workflows, and prompt engineering to improve relevance while reducing hallucinations.
I also spent a lot of time refining the orchestration workflow itself. The goal was to create a system that could move cleanly from retrieval to analysis to structured output generation while maintaining a usable workflow experience.
Accomplishments that we're proud of
One of the biggest accomplishments was building a workflow capable of transforming large amounts of unstructured healthcare filing data into organized diligence-style intelligence outputs.
I’m also proud of creating a project that combines retrieval systems, semantic search, and AI orchestration into a cohesive healthcare intelligence workflow rather than a standalone AI assistant.
Another accomplishment was designing outputs that feel structured and enterprise-oriented, including AI-generated diligence reports and risk-focused summaries.
What we learned
This project reinforced how important orchestration and retrieval quality are in enterprise AI systems, especially in document-heavy industries like healthcare and finance.
I also learned that useful AI workflows depend heavily on:
retrieval quality, context management, workflow structure, and presentation of outputs,
not just model capability alone.
One of the biggest takeaways from the project was:
Relevant Context+Semantic Retrieval→Higher Quality AI Analysis
The project also highlighted how agentic workflows can help organize complex document analysis tasks into more scalable and structured intelligence pipelines.
What's next for MedIntel AI
Future versions of MedIntel AI will focus on:
Multi-agent orchestration workflows Human-in-the-loop review systems Automated diligence memo generation Competitor benchmarking dashboards Risk scoring systems Multi-document comparative analysis Expanded healthcare reimbursement intelligence
The long-term goal is to continue developing MedIntel AI into a scalable enterprise healthcare intelligence platform capable of supporting complex regulatory and operational analysis workflows.
Log in or sign up for Devpost to join the conversation.