About the Project Inspiration
Accessing government policies, social welfare rules, and public programs is often confusing, inaccessible, and full of legal jargon. Many people simply don’t apply because they can’t understand the eligibility or process. We were inspired to build something that translates bureaucracy into conversation — so every citizen can understand and act on their rights.
What it Does
You upload a policy or ordinance (PDF, document text).
You ask questions in plain language (e.g. “Am I eligible for housing assistance?”).
The system parses and reasons through the document, extracts relevant rules, and gives you a personalized answer with steps.
You can also view the AI’s reasoning trace — how it arrived at that answer by referencing sections of the document.
How We Built It
Document Ingestion & Embeddings: We chunk text, create embeddings (vector store) to allow semantic search.
Agent Architecture:
Parser Agent — splits and understands policy text
Eligibility Agent — reasons about user context + rules
Explainer Agent — translates logic into conversational output
Frontend: React + Tailwind + animated UI components
API Layer: Backend endpoints coordinate the agents and data
Deployment: Hosted on Vercel / serverless backend
In development, we used prompt engineering loops and test documents to refine each agent’s accuracy.
Challenges We Ran Into
Getting AI to avoid hallucination when documents are ambiguous
Handling scanned or low-quality PDFs and extracting structured text
Dealing with conflicting rules across multiple policies
Managing user context in conversation (keeping state, asking clarifiers)
Performance — latency when reasoning over large documents
Accomplishments We’re Proud Of
Built a working MVP in under 48 hours
Successfully parsed real policy documents and answered user queries
Designed a reasoning trace visualization to provide auditability
Demonstrated agentic AI collaboration — humans prompt, AI designs workflows
Created a UI experience that feels credible, responsive, and alive
What We Learned
How to chain AI agents effectively (parser → reasoner → explainer)
Techniques for semantic retrieval + prompt refinement
UX lessons in transparency: users want to see why the AI gives an answer
The importance of error-handling and fallback flows (when AI isn’t confident)
What’s Next for gov-clarity-bot
Multilingual support — bringing it to multiple languages
Voice / multimodal input/output (ask by voice, get spoken response)
Expand domain coverage (local governments, municipal policies, taxes)
Collaborations with civic groups or governments to adopt it as a public tool
Add continuous document ingestion — daily auto-updates when new policies are published
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