Built A Global Legal AI Assistant Using LLM & Web Scraping – Powering Legal Intelligence Across 120+ Countries
By building a LegalTech AI platform powered by LLMs and large-scale web scraping, Shrijan Tech helped automate legal data extraction from 120+ government bodies and trained a custom AI assistant to deliver real-time legal summaries and compliance insights across 25+ legal categories.
Overview
True transformation begins with commitment a willingness to take on complexity, understand the price of innovation, and persist until excellence is achieved.
Our client, a forward-thinking LegalTech startup, had a vision to make global law accessible to everyone from corporate counsels to compliance officers. Yet, the challenge was immense: legal data scattered across 120+ government bodies, written in different formats and languages, and locked behind outdated systems. Shrijan Tech partnered with them to turn this ambition into reality. Through large-scale data scraping, domain-specific LLM training, and intelligent AI automation, we built a global Legal AI assistant capable of reading, summarizing, and comparing laws across borders. This project wasn’t just a technological achievement it was a statement of persistence, precision, and the belief that access to legal knowledge should have no boundaries.
LegalTech AI Startup
Business
Europe
Location
Key Challenges
Solutions Provided
Developed Python-based web scrapers capable of collecting and parsing structured and unstructured legal data from 120+ official government portals worldwide.
Implemented OCR and text-classification pipelines to digitize scanned PDFs, tagging and categorizing data under 25+ legal domains such as corporate law, IP, and compliance.
Trained a domain-specific Legal Large Language Model using proprietary data, enhancing context retention, accuracy, and multilingual understanding.
Integrated an AI chatbot powered by LangChain and OpenAI APIs, allowing users to query, summarize, and compare laws across jurisdictions in real time.
Built a knowledge layer using Elasticsearch and Pinecone for semantic search, enabling contextual retrieval and verified legal citations.
Deployed the system on AWS using Lambda, EC2, and Docker for auto-scaling, ensuring high performance even with millions of records.
Technology Stack
