Juris AI — Multi-Agent Legal Command Center
A supervisor-orchestrated AI system that automates client intake, legal research, scheduling, and follow-up for law firms.
Overview
Juris AI is a full-stack legal-tech dashboard built around a Supervisor Agent architecture — one central engine that delegates work across five specialized sub-agents, each handling a distinct part of a law firm's client pipeline. Instead of a single chatbot, this is a coordinated multi-agent workflow: intake, research, scheduling, outreach, and follow-up all operating under one live control center.
Core Features
🧠 Supervisor Agent Engine
Central dashboard that monitors and orchestrates all five sub-agents in real time, with live status indicators (Operational / Ready / Active / Standing By) for each one.
📋 Qualification Agent (Client Intake)
Captures client name, email, and practice area, then runs an AI evaluation on case details to classify eligibility and route new leads automatically.
📚 Research Agent (RAG-Powered)
A retrieval-augmented generation engine connected to an internal case-law vector database. Lawyers can query legal precedents in plain English and get relevant results pulled directly from indexed case law.
📅 Meeting Agent
Schedules attorney–client consultations directly from the dashboard, capturing date, time, and client details, and syncing them to an availability log.
✉️ Outreach Agent
Generates personalized client email drafts from a few key talking points — ready to review and dispatch without starting from a blank page.
🔔 Followup Agent
Tracks clients with pending responses or outstanding document signatures and triggers automated reminder emails/SMS with one click.
📊 Live Analytics Dashboard
Visualizes supervisor delegations and RAG query volume over the week, plus a live multi-agent activity log showing every action each agent takes in real time.
Tech Stack
Frontend: Custom HTML/CSS/JavaScript dashboard — dark command-center UI with a modular card layout
Backend: Python
AI Layer: RAG (Retrieval-Augmented Generation) pipeline over an internal legal document vector store
Architecture: Supervisor–sub-agent orchestration pattern (one router agent delegating to task-specific agents)
What's Included
Full multi-agent dashboard (Supervisor, Intake, Research, Meeting, Outreach, Followup)
RAG-based legal precedent search connected to a case-law database
Real-time activity logging across all agents
Responsive, professional dark-themed UI built specifically for legal workflows
Clean, modular codebase — easy to extend with new agents or connect to a real case-management backend
Ideal For
Law firms and legal-tech startups looking to automate repetitive parts of client intake and case management — or as a foundation to extend into a full practice-management platform.
Available Add-Ons
Real user authentication & role-based access (partners, associates, paralegals)
Integration with a live case-management database
Custom sub-agents (billing, contract review, e-discovery, etc.)
Deployment & hosting setup
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:→ A Coder agent writes the actual implementation
→ A Reviewer agent — a completely separate AI persona — reviews it for correctness, edge cases, and security issues
→ If rejected, the Coder revises based on specific feedback (up to 3 rounds)
→ Once approved, a Tester agent writes real pytest tests from scratch
→ Tests run in a sandbox. If they fail, the Coder fixes the code and the cycle repeats
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I built an AI agent that doesn't just chat — it actually does things. 🛰️
Most "AI chatbot" projects stop at answering questions. I wanted to go further, so I built an autonomous customer support agent that can:
✅ Look up real orders from a database
✅ Calculate and process refunds — with a confirmation step before anything is committed
✅ Create support tickets automatically
✅ Search actual policy documents (RAG) instead of guessing answers
✅ Detect when it's stuck and escalate to a human
✅ Understand English, Urdu, and Roman Urdu — automatically
✅ Take voice input and reply back with voice
✅ Resist prompt-injection attempts
It's powered by Groq (Llama 3.3 70B) with real function-calling — not hardcoded if/else logic. The agent decides which tool to use, when to chain multiple tools together, and when a human needs to step in.
I also built a live analytics dashboard on top of it, so every ticket, refund, and escalation is tracked in real time.
This project taught me more about how production AI agents actually work than any tutorial could — tool orchestration, guardrails, and the messy reality of getting LLMs to behave reliably.
🔗 Code on GitHub: https://github.com/tehseenaizaz2/ai-support-agent.git
(https://github.com/tehseenaizaz2/ai-support-agent.git)🎥 Demo: https://ai-support-agent-cl72uu3tk9vdxgoeufifxu.streamlit.app/
#AI #MachineLearning #LLM #Groq #Python #AIAgents #OpenToWork #Freelance #Streamlit
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⚡ DocuMind Intelligence Hub — RAG System
A lightweight, modern Retrieval-Augmented Generation (RAG) system designed for instant PDF querying and semantic document analysis.
✨ Key Features
🚀 Blazing Fast Answers: Powered by Groq Llama 3.1 8B Instant LLM.
🔍 Semantic Vector Search: In-memory ChromaDB integration for exact context retrieval.
📍 Source Citations: Grounded answers with page-level document citations to prevent hallucinations.
🎨 Modern UI: Responsive and clean interface built with Streamlit and custom styling.
🛠️ Tech Stack
Frontend: Streamlit
Vector Database: ChromaDB
LLM Engine: Groq API (Llama 3.1)
PDF Parser: PyPDF
https://document-inteliigence-rag-2tjute5rb5zdhxesfbujew.streamlit.app/
(https://document-inteliigence-rag-2tjute5rb5zdhxesfbujew.streamlit.app/)https://github.com/tehseenaizaz2/document-inteliigence-rag.git
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