Landing Page

CoBridge

A comprehensive solution for automating geo-specific regulatory compliance checks using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). This system addresses TikTok's challenge of proactively implementing legal guardrails before feature launches by turning regulatory detection from a blind spot into a traceable, auditable output.

Problem Statement

As TikTok operates globally, every product feature must dynamically satisfy dozens of geographic regulations – from Brazil's data localization laws to GDPR compliance. The challenge is to build a prototype system that utilizes LLM capabilities to flag features that require geo-specific compliance logic, empowering TikTok to:

Proactively implement legal guardrails before feature launch
Generate auditable evidence proving features were screened for regional compliance needs
Confidently respond to regulatory inquiries with automated traceability

Without automated compliance checking, potential risks include:

Legal exposure from undetected compliance gaps
Reactive firefighting when auditors or regulators inquire
Manual overhead in scaling global feature rollouts

Solution Overview

Our system transforms regulatory compliance from reactive manual processes to proactive automated governance. The platform leverages advanced AI techniques to analyze documents against an extensive database, providing immediate feedback on potential compliance conflicts through bidirectional analysis.

Key Features

Bidirectional Document Analysis: Upload either law documents or feature documents for comprehensive compliance checking
Feature Impact Assessment: When law documents are uploaded, identify existing features that may require modifications
Compliance Validation: When feature documents are uploaded, flag potential conflicts with existing regulations
Comprehensive Reporting: Generate detailed compliance reports with clear reasoning and evidence trails
Multi-Format Export: Download reports as PDF, Excel, or view directly in the browser
Audit Trail Generation: Maintain complete traceability for regulatory inquiries
Real-Time Processing: Immediate analysis and feedback on uploaded documents

How we built it

We architected CoBridge using a modern full-stack approach with AI at its core:

Frontend: Built with React.js, Tailwind CSS, ShadCN component library, Radix UI primitives, and Framer Motion for smooth animations, creating an intuitive interface for document upload and report visualization.

Backend: Implemented using FastAPI for high-performance Python APIs and Go services for efficient data processing, with MongoDB serving as our document database for compliance records and audit trails.

AI Pipeline: Integrated Gemini LLM for intelligent document parsing and analysis, FAISS vector store for semantic similarity searches, and RAG (Retrieval-Augmented Generation) for context-aware compliance analysis. The system converts documents to embeddings, performs similarity matching, and uses retrieved context to generate accurate compliance assessments.

Architecture: Designed a bidirectional analysis engine where each document type follows a specific processing flow – feature documents query the law database, while law documents query the feature database, enabling comprehensive cross-impact analysis.

Challenges we ran into

Data Processing Complexity: Parsing diverse document formats (PDFs, Word docs, legal text) and extracting structured information required extensive preprocessing and robust error handling.

Embedding Quality: Achieving accurate semantic similarity between legal language and technical feature descriptions was challenging, requiring careful tuning of similarity thresholds and embedding models.

Bidirectional Logic: Implementing the dual-direction analysis (laws→features, features→laws) while maintaining data consistency and avoiding circular references required careful architectural planning.

Legal Accuracy: Ensuring the AI-generated compliance analysis was reliable enough for real-world regulatory scenarios while providing clear reasoning for each recommendation.

Accomplishments that we're proud of

Bidirectional Intelligence: Successfully implemented a system that works both ways – analyzing features against laws AND laws against features, providing comprehensive compliance coverage that goes beyond traditional one-directional tools.

Production-Ready Architecture: Built a scalable system with proper separation of concerns, robust error handling, and audit trail capabilities suitable for enterprise deployment.

Intelligent Report Generation: Created a reporting system that generates professional, actionable compliance reports with detailed reasoning, making complex legal analysis accessible to product teams.

Seamless User Experience: Despite the complex AI backend, we maintained an intuitive interface where users simply upload documents and receive immediate, comprehensive analysis.

Real-World Applicability: Developed a solution that directly addresses TikTok's stated needs while being generalizable to any company operating across multiple jurisdictions.

What we learned

AI-Legal Integration: Discovered the nuances of applying AI to legal compliance, including the importance of providing clear reasoning and maintaining audit trails for regulatory scrutiny.

Cross-Domain Analysis: Learned how to effectively bridge technical product documentation and legal regulatory language using advanced embedding techniques and RAG methodology.

Scalable AI Architecture: Gained insights into building AI systems that maintain performance while handling diverse document types and complex relational queries between different data domains.

User-Centered Compliance: Understood that successful compliance tools must balance comprehensive analysis with practical usability, ensuring that detailed legal insights translate into actionable guidance.

Regulatory Technology Gap: Recognized the significant opportunity in applying modern AI techniques to traditional compliance processes, where manual effort currently dominates.

Architecture

System Flow

alt text

Feature Document Upload Flow

Document Upload: Users upload feature artifact documents through the web interface
Backend Processing: Documents are sent to the backend server for initial processing
LLM Parsing: Feature documents are parsed through Gemini LLM to extract structured information
Embedding Generation: Parsed content is converted to embeddings for similarity matching
Law Database Query: FAISS vector store is queried for law documents exceeding similarity threshold
RAG Processing: Retrieved law documents serve as context for LLM analysis
Compliance Analysis: LLM identifies potential law violations and compliance requirements for the new feature
Data Storage: Results are stored in MongoDB and feature is added to feature database
Report Generation: Frontend generates compliance reports showing regulatory conflicts

Law Document Upload Flow

Document Upload: Users upload law or regulation documents through the web interface
Backend Processing: Law documents are sent to the backend server for processing
LLM Parsing: Law documents are parsed through Gemini LLM to extract regulatory requirements
Embedding Generation: Parsed legal content is converted to embeddings
Feature Database Query: FAISS vector store is queried for existing features that may be affected
RAG Processing: Retrieved feature documents serve as context for impact analysis
Impact Analysis: LLM identifies existing features that may need modification due to the new regulation
Data Storage: Law is added to the law database and analysis results stored in MongoDB
Report Generation: Frontend generates impact assessment reports showing affected features

Technology Stack

Frontend

React.js: Modern web application framework for responsive user interfaces
Tailwind CSS: Utility-first CSS framework for rapid UI development
ShadCN: Component library for consistent design patterns
Radix UI: Accessible component primitives
Framer Motion: Animation library for enhanced user experience

Backend

FastAPI: High-performance Python web framework for API development
Go: Efficient backend services for data processing
MongoDB: Document database for storing compliance data and audit trails

AI/ML Components

Gemini LLM: Advanced language model for document parsing and compliance analysis
FAISS Vector Store: Efficient similarity search for regulation and feature matching
RAG (Retrieval-Augmented Generation): Enhanced context-aware analysis
Embedding Models: Document vectorization for semantic similarity

Core Functionality

RAG-Powered Bidirectional Analysis Engine

Law-to-Feature Analysis

Input: New law or regulation document
Process: RAG retrieval against comprehensive feature database
Output: List of existing features that may require modification or review

Feature-to-Law Analysis

Input: New feature documentation
Process: RAG retrieval against existing law database
Output: List of potentially applicable laws and compliance requirements

Intelligent Dataset Management

Duplicate Detection: Identifies existing laws or features to prevent redundancy
Version Control: Manages law updates and feature revisions
Automated Parsing: Converts documents to standardized format with detailed labels
Cross-Reference Updates: Maintains relationships between laws and affected features

Data Management

Dual Storage Architecture

Vector Store (FAISS): Optimized for semantic similarity searches between laws and features
MongoDB: Structured data storage for compliance records, audit trails, and document metadata

Dataset Categories

Laws Database: Comprehensive collection of geo-specific regulations and legal requirements
Features Database: Repository of product features with their compliance status and history

Features and Capabilities

Document Upload and Processing

Support for multiple document formats (PDF, DOC, TXT)
Automatic classification of document type (law vs feature)
Intelligent parsing with structured output generation
Real-time processing status updates

Bidirectional Compliance Analysis

New Feature Analysis: Identify regulatory requirements for proposed features
Regulatory Impact Assessment: Determine which existing features are affected by new laws
Cross-Impact Visualization: Show relationships between laws and features
Historical Compliance Tracking: Maintain audit trail of all compliance decisions

Compliance Report Generation

Browser Viewing: Interactive web-based report interface
PDF Export: Professional compliance reports for stakeholders
Excel Export: Structured data for further analysis
Detailed Analysis: Clear reasoning for each identified compliance requirement or feature impact

Advanced Analytics

Similarity threshold configuration for precision tuning
Historical compliance trend analysis
Risk assessment scoring for both features and regulations
Geographic regulation mapping and feature impact zones

Installation and Setup

Prerequisites

Node.js (v18 or higher)
Python (v3.9 or higher)
Go (v1.19 or higher)
MongoDB instance
Gemini API access

Frontend Setup

# Clone the repository
git clone [repository-url]
cd compliance-automation-frontend

# Install dependencies
npm install

# Start development server
npm run dev

Backend Setup

# Python FastAPI backend
cd backend/python
pip install -r requirements.txt
uvicorn main:app --reload

# Go services
cd ../go
go mod tidy
go run main.go

Database Configuration

# MongoDB setup
mongod --dbpath /path/to/data

# Initialize vector store with law and feature databases
python scripts/init_vector_store.py

Usage

Basic Workflow

Uploading Feature Documents

Feature Upload

Navigate to the upload interface
Select "Feature Document" option
Upload feature artifact document (PRD, TRD, specifications)
System analyzes against existing law database

Compliance Review

Monitor real-time processing status
Review identified regulatory requirements
Examine detailed reasoning for each compliance flag
View geographic scope of applicable regulations

Report Generation
- View comprehensive compliance report in browser
- Export to PDF for stakeholder distribution
- Download Excel format for compliance tracking

Uploading Law Documents

Law Upload

Navigate to the upload interface
Select "Law/Regulation Document" option
Upload legal document or regulation text
System analyzes impact on existing feature database

Impact Assessment

Review list of potentially affected features
Examine detailed analysis of required modifications
Assess priority and timeline for compliance updates
View feature-specific compliance gaps

Action Planning
- Generate feature modification recommendations
- Export impact assessment reports
- Track compliance update progress

Advanced Features

Dataset Management

Add new regulations to the law database with automatic feature impact analysis
Update existing feature classifications with regulatory compliance status
Manage version control for both regulatory changes and feature updates
Maintain cross-references between laws and affected features

Compliance Monitoring

Set up automated compliance checking for new features against all regulations
Configure notification systems for regulatory changes affecting existing features
Maintain comprehensive audit logs for regulatory inquiries
Track compliance status across all features and regulations

Output Examples

The system generates comprehensive reports tailored to the document type uploaded:

Feature Compliance Reports

Executive Summary: Compliance status overview for the new feature
Regulatory Requirements: Specific laws and regulations that apply
Geographic Compliance: Regional requirements and restrictions
Implementation Guidance: Actionable steps for compliance adherence
Risk Assessment: Potential legal exposure levels

Regulatory Impact Reports

Affected Features Summary: List of features requiring review or modification
Impact Analysis: Detailed breakdown of required changes per feature
Priority Assessment: Risk-based prioritization of compliance updates
Timeline Recommendations: Suggested implementation schedule
Resource Requirements: Estimated effort for compliance updates