AI Contract Risk Flagging Tool

"Don't Sign Blind."

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Inspiration

Legal contracts are often long, complex, and difficult for non-legal professionals to understand. Many individuals and small businesses sign agreements without fully understanding hidden risks such as liability clauses, penalties, termination conditions, or indemnification obligations.

We were inspired to build the AI Contract Risk Flagging Tool to make contract analysis more accessible, faster, and easier using Artificial Intelligence and Natural Language Processing (NLP). Our goal was to create a smart assistant that helps users quickly identify risky legal clauses before signing important agreements.

What it does

The AI Contract Risk Flagging Tool allows users to upload legal contracts in PDF or DOCX format and automatically performs clause-by-clause risk analysis.

Key Features Upload legal contracts Extract text from PDF/DOCX files AI-based clause analysis Risk highlighting using: 🔴 High Risk 🟡 Medium Risk 🟢 Low Risk Plain English explanation for each flagged clause Overall contract risk score Side-by-side clause and explanation view Downloadable PDF risk assessment report Modern interactive dashboard UI

The system helps users understand potentially dangerous legal terms without needing advanced legal knowledge.

How we built it

We built the project using a full-stack AI web application architecture.

Frontend

We used: React JavaScript Axios CSS styling

The frontend handles: File uploads API communication Dashboard visualization Risk cards PDF report downloads Backend

We used: Flask Python

The backend handles: Contract processing AI analysis Risk scoring Report generation REST API endpoints AI/NLP Layer

The AI engine uses: Rule-based NLP analysis Legal keyword detection Clause classification Risk scoring algorithms File Processing

We used: pdfplumber for PDF extraction python-docx for DOCX parsing Report Generation

We used: ReportLab for downloadable reports

Challenges we ran into

One of the biggest challenges was handling different document formats and accurately extracting text from contracts. PDFs often contain inconsistent formatting, which made clause extraction difficult.

We also faced challenges while:

Designing the AI risk classification logic Connecting the React frontend with the Flask backend Handling file upload errors and API debugging Creating a clean and professional dashboard UI Generating downloadable PDF reports dynamically

Another major challenge was simplifying complex legal language into understandable plain-English explanations for users without legal expertise.

Accomplishments that we're proud of

We are proud that we successfully built a complete end-to-end AI-powered legal analysis platform from scratch.

Major accomplishments include: Building a working full-stack AI application Successfully analyzing legal contracts automatically Implementing intelligent risk categorization Creating a modern SaaS-style dashboard Generating downloadable AI risk reports Designing a scalable architecture for future AI upgrades

We are especially proud of transforming a complex legal analysis process into an easy-to-use and user-friendly experience.

What we learned

Through this project, we learned:

Full-stack application development REST API integration React state management Flask backend development PDF and DOCX document processing NLP and AI-based text analysis Risk scoring systems Frontend UI/UX design Error handling and debugging

We also gained deeper understanding of how AI can be applied in the LegalTech industry to solve real-world problems.

What's next for AI Contract Risk Flagging Tool

We plan to expand the project into a more advanced AI legal assistant platform.

Future improvements include: Integration with Large Language Models like Claude or GPT Semantic legal clause understanding AI-generated contract summaries OCR support for scanned contracts Multi-language contract analysis Chat-with-contract functionality Real-time legal recommendations Cloud deployment and authentication Compliance checking and legal benchmarking

We also plan to integrate Legal-BERT and transformer-based models for more accurate legal reasoning and contextual understanding.

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