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User-friendly interface for uploading documents and describing situations.
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Users can upload notices, forms, policies, and official documents for analysis.
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AI extracts key deadlines, requirements, and next steps from uploaded documents.
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ActionBridge AI transforms complex information into clear, actionable guidance.
Inspiration
Most people who get important documents (such as scholarship letters, government postings, college admissions info, application directions, etc.) have trouble figuring out exactly what those documents say.
Important documents are usually written in formal, technical language so that the author can express all the possible ways that the content could be interpreted by the reader. This makes it very difficult for the reader to know which parts of the document relate directly to him/her; whether he/she has to do something about each part of the document; how much time he/she will have to do something related to some of the parts of the document; and if there are other things he/she needs to do to meet one of the "requirements" mentioned in the document.
Our main objective when we started this project was to build an artificial intelligence-based tool to bridge the gap between reading informational material, and knowing what you need to do next. We didn't want to just summarize the contents of documents - instead, we wanted users to have a way to understand what is most relevant to them about a particular piece of information. Then, based on that understanding, we hoped users would feel confident enough to decide what they needed to do next.
Our concept fits with the global USAII AI Hackathon's theme of helping individuals locate and use support, understand information, and make good decisions about various situations.
What It Does
ActionBridge AI lets users either upload a pdf document containing information that they wish to gain clarity from about a specific issue; provide background information describing their specific situation; or both.
Using a combination of natural language processing techniques, and machine learning algorithms, ActionBridge AI processes the provided information and produces an easy-to-understand description of that information in plain English. The user gains clarity around:
what the document is talking about which pieces of information are most important whether any of the requirements listed in the document apply to them what options they might have for their next course of action where else they might look to obtain additional assistance
Unlike many applications where users are left to decipher complex language and then try to figure out what they are supposed to do with that new-found knowledge, ActionBridge AI translates information into actionable advice.
How We Built It
The project was built using:
Python Streamlit PyPDF Groq API Llama 3.3 70B Versatile
The workflow is:
User uploads a PDF document and/or enters a question. PyPDF extracts text from the uploaded document. The extracted document content and user question are combined into a single context. The Groq-hosted Llama model analyzes the information. ActionBridge AI generates a plain-language explanation and actionable guidance.
The application is deployed through Streamlit Community Cloud and can be accessed through a web browser.
Challenges We Faced
One of the biggest challenges was ensuring that the system provided useful guidance rather than behaving like a generic chatbot.
We spent significant time refining prompts so that the AI remained focused on helping users understand documents, requirements, situations, and next steps.
Another challenge was handling different document types and ensuring extracted text remained understandable after PDF processing.
We also worked on creating a simple and accessible user experience that allows users to upload documents and ask questions without technical knowledge.
What We Learned
Through this project we learned how AI can be used as a decision-support tool rather than simply a conversational assistant.
We gained experience with prompt engineering, document processing, cloud deployment, and designing AI systems that focus on user understanding and decision-making.
Most importantly, we learned that many real-world problems are not caused by a lack of information but by a lack of clarity.
Future Improvements
Future versions of ActionBridge AI could include:
Support for images and scanned documents Multilingual document understanding Personalized resource recommendations Better document classification Conversation history and follow-up guidance Integration with public service and support platforms
Our long-term goal is to help more people understand important information and confidently take action when it matters most.
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