Building AI powered Legal Voice AI Assistant

This is a submission for the AssemblyAI Voice Agents Challenge

What I Built

An AI-powered voice assistant specialized in the legal industry.

It uses AssemblyAI for transcription where the lawyers or everyday users can speak and it transcribes the spoken words to be analysed by the system. RAG (Retrieval-Augmented Generation) for grounded responses, and tracks conversation quality through logs, ratings, and corrections.
Prompt Category - Domain Expert Voice Agent

Challenge

The legal industry is characterized by its high volume of documentation, complex research requirements, and the critical need for accuracy and efficiency. Legal AI voice assistants, powered by advanced speech-to-text and natural language processing (NLP) technologies, offer a compelling solution to many of these challenges.
Here's a detailed reason why we need legal AI voice assistants in the law industry:
Legal Research and Information Retrieval: The volume of legal precedents, statutes, regulations, and case law is immense. Traditional legal research can be a laborious process of sifting through countless documents. A voice assistant, integrated with legal databases, can allow lawyers to verbally query complex legal questions, retrieve relevant case law, statutes, and articles, and even summarize key findings. For example, a lawyer could ask, "Find all cases related to breach of contract in intellectual property disputes in the state of California from the last five years," and the AI could quickly surface highly relevant results.

Demo

GitHub Repository

Github link

Technical Implementation & AssemblyAI Integration

This is how I leverage AssemblyAI Universal Streaming API to transcribe the speech from the users to text that can be understood by the backend system (RAG module).

import requests

ASSEMBLYAI_API_KEY = "your_assemblyai_api_key"
headers = {"authorization": ASSEMBLYAI_API_KEY}

def transcribe_audio_url(audio_url: str):
    endpoint = "https://api.assemblyai.com/v2/transcript"
    model = "universal"
    response = requests.post(endpoint, json={"audio_url": audio_url,"speech_model": model}, headers=headers)
    return response.json()