Inquire.ai - Autonomous AI Research Assistant

Intelligent AI system for analyzing documents, extracting KPIs, searching the web and generating comprehensive reports using LangChain and Google Gemini.

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GenAI Agent Development
LangChain Integration
Retrieval Augmented Generation (RAG)

Inquire.ai interface showing document analysis and research capabilities

Autonomous Research Made Simple

Inquire.ai transforms the way professionals conduct research by providing an intelligent AI assistant that can analyze documents, extract key insights, and generate comprehensive reports. Built with LangChain and powered by Google Gemini, it offers autonomous decision-making capabilities that adapt to different research needs.

The platform combines advanced document processing with web search capabilities, enabling users to gather insights from both uploaded documents and real-time web information. With project-based organization and intelligent tool routing, it streamlines the entire research workflow from document upload to final report generation.

Key Features

A comprehensive set of autonomous tools designed to enhance research productivity

Project Management

Organize documents into different projects with isolated chat histories and context

Document Analysis

Upload PDFs and TXT files with intelligent chunking and searchable sections

LangGraph Agent

React-style autonomous agent with custom decision logic for tool selection

KPI Extraction

Extract key performance indicators and metrics from documents automatically

Report Generation

Generate comprehensive professional reports from document findings

Web Search Integration

Real-time web search using Tavily API for external information retrieval

Technology Stack

Built with cutting-edge AI and document processing technologies

Streamlit

LangChain

Google Gemini AI

ChromaDB

Tavily Search API

Python

System Architecture

The platform follows a modular architecture with distinct layers for document processing, AI agent orchestration, vector storage, and web search integration.

LangChain handles document loading and chunking, while ChromaDB provides efficient vector storage and retrieval. The LangGraph agent coordinates tool selection and execution based on user queries and context.

Architecture Overview:
• Frontend: Streamlit Web Interface
• AI Engine: LangGraph Autonomous Agent
• Document Processing: LangChain
• Vector Database: ChromaDB
• LLM: Google Gemini AI
• Web Search: Tavily Search API

Implementation Process

A systematic approach to building an autonomous AI research assistant

Document Processing Setup

Implemented LangChain document loaders and chunking strategies for PDFs and text files

Vector Database Integration

Set up ChromaDB for project-based document storage and efficient retrieval operations

LangGraph Agent Development

Built autonomous agent with React-style decision making and custom tool selection logic

AI Tool Implementation

Developed specialized tools for context retrieval, summarization, KPI extraction, and reporting

Web Search Integration

Integrated Tavily Search API for real-time external information gathering

Project Impact

Delivering significant improvements in research efficiency and insight generation

90%

Time Reduction

Autonomous document analysis reduces manual research time by up to 90%

Tool Integration

Integrated five specialized AI tools for comprehensive research workflows

100%

Autonomous Operation

Fully autonomous tool selection and execution based on user intent

Ready to Automate Your Research?

Experience the power of autonomous AI research with Inquire.ai. Upload your documents, ask questions, and let the AI agent handle the complex analysis and reporting tasks.

Explore the Project