Inspiration
In the world of tax litigation, one of the biggest challenges is deciding whether a case is worth appealing. Every unnecessary appeal drains public resources, clogs the judicial system, and delays justice.
What it does
• Outcome Prediction: Uses machine learning trained on past judicial decisions to predict the likelihood of success in higher courts. • Legal Reasoning Engine: Analyzes relevant case law, statutory provisions, and tribunal rulings to generate case-specific legal arguments. • Forecasted Opinions: Generates a sample higher court judgment, outlining likely reasoning for upholding or dismissing the appeal. • Appeal Optimization: Offers data-backed recommendations to reduce frivolous appeals, enabling smarter closure decisions.
How we built it
We’re building an AI-powered Tax Litigation Co-Pilot — a decision intelligence tool that empowers tax officers with real-time, objective, and legally-grounded recommendations on whether to pursue appeals. We used the following tech stack
- Python
- Flask
- Open AI
- Milvis DB
- React
- Vercel
Challenges we ran into
- How to upload large PDFs and generate embeddings
- First time working with a lot of new tech like Vector database and embeddings. So involved a bit of learning curve
Accomplishments that we're proud of
- Learned a lot of new tech
What we learned
- RAG
- Python
What's next for Taxation Appeals Copilot for Lawyers
- Turn this into a chatbot and make it more interactive
- Add a voice agent to interact
- Introduce cloud blob storage for files
- Implememt this with GraphRAG
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