MultiQuery Pilot

An AI-powered customer interaction infrastructure that combines conversational AI, Retrieval-Augmented Generation (RAG), semantic search, voice AI, and real-time human escalation into a scalable SaaS platform.

---

🚀 Overview

MultiQuery Pilot is a full-stack AI-powered customer support and interaction platform designed for modern digital businesses.

The platform allows organizations to embed an intelligent AI assistant directly into their websites using a lightweight script. Customers can interact through chat or voice, while the system retrieves information from enterprise knowledge bases and generates context-aware responses using advanced language models.

If the AI cannot resolve a query, the conversation is seamlessly escalated to a human operator through a real-time dashboard.

The platform also integrates Vapi Voice AI infrastructure to support real-time inbound voice conversations and outbound AI-assisted calls for reminders, onboarding, follow-ups, and automated workflows.

---

✨ Core Features

🤖 AI-Powered Support

• Context-aware conversational AI
• Real-time response generation
• AI-assisted customer interaction workflows

📚 Retrieval-Augmented Generation (RAG)

• Upload PDFs, manuals, FAQs, and documents
• Semantic vector search
• Grounded AI responses
• Reduced hallucination

🎙️ Voice AI Integration (Vapi)

• Real-time browser voice calls
• Inbound AI conversations
• Outbound AI-assisted calls
• Speech-to-text and text-to-speech workflows
• AI-powered voice automation

🧠 Semantic Search Engine

• Vector embeddings
• Similarity-based document retrieval
• Enterprise-scale knowledge indexing

👨‍💻 Human Escalation System

• AI → Human handoff
• Real-time operator dashboard
• Live conversation management

🏢 Multi-Tenant SaaS Architecture

• Organization-level isolation
• Separate knowledge bases
• Scalable enterprise deployment

🌐 Embeddable Widget

Integrate MultiQuery Pilot into any website using a single script:

<script src="widget.js"></script>

---

🏗️ System Architecture

Customer Website
        ↓
Embed Script
        ↓
AI Widget (Chat + Voice)
        ↓
AI Intelligence Layer
        ↓
Knowledge Retrieval Engine (RAG)
        ↓
Convex Backend
        ↓
Operator Dashboard

---

⚡ How It Works

1. Widget Integration

Businesses integrate the widget into their websites using a lightweight embed script.

2. User Interaction

Customers communicate through:

• Chat
• Voice

3. AI Processing

The AI engine:

• understands user intent
• converts queries into embeddings
• performs semantic retrieval

4. Knowledge Retrieval

The system retrieves the most relevant document chunks from uploaded enterprise documents.

5. Response Generation

Retrieved context is passed to the language model to generate grounded responses.

6. Human Escalation

If unresolved:

• conversations are escalated
• human operators take over in real time

---

🧠 RAG Pipeline

User Query
    ↓
Embedding Generation
    ↓
Vector Similarity Search
    ↓
Retrieve Relevant Chunks
    ↓
Pass Context to LLM
    ↓
Generate Context-Aware Response

---

🎙️ Voice AI Flow (Powered by Vapi)

Inbound Voice Calls

Users can directly initiate real-time AI voice conversations through the support widget.

User Speaks
    ↓
Speech-to-Text
    ↓
AI Reasoning
    ↓
Response Generation
    ↓
Text-to-Speech

Example Use Cases

• Product support
• Troubleshooting
• Customer onboarding
• Real-time assistance

---

Outbound AI Calls

The platform can also trigger automated outbound AI-assisted calls.

System Triggers Call
    ↓
AI Initiates Conversation
    ↓
Real-Time Interaction
    ↓
Conversation Storage

Example Use Cases

• Appointment reminders
• Payment notifications
• Customer follow-ups
• Workflow automation
• Proactive engagement

---

🏢 Enterprise Scalability

MultiQuery Pilot is designed to support enterprise-scale knowledge systems.

Large documents are:

• chunked into smaller segments
• converted into semantic embeddings
• indexed inside vector storage

Instead of processing entire documents, the system retrieves only the most semantically relevant information.

This enables:

• scalable enterprise deployment
• low-latency retrieval
• efficient large knowledge base handling

---

🛠️ Tech Stack

Frontend

• Next.js
• React
• Tailwind CSS
• shadcn/ui
• Radix UI

Backend

• Convex
• TypeScript

AI Stack

• OpenAI GPT Models
• Embedding Models
• Retrieval-Augmented Generation (RAG)

Voice Infrastructure

• Vapi AI

Authentication

• Clerk

Deployment

• Vercel

---

📂 Monorepo Structure

Echo/
├── apps/
│   ├── embed/
│   ├── widget/
│   └── web/
│
├── packages/
│   ├── backend/
│   ├── ui/
│   ├── eslint-config/
│   └── typescript-config/
│
├── turbo.json
├── pnpm-workspace.yaml
└── package.json

---

🔥 Why MultiQuery Pilot is Unique

Unlike traditional chatbot systems, MultiQuery Pilot combines:

• AI reasoning
• Retrieval-Augmented Generation
• semantic enterprise search
• voice communication
• real-time human escalation
• SaaS-ready architecture

into one unified platform.

---

📈 Business Benefits

✅ Reduces support costs
✅ 24/7 AI assistance
✅ Faster customer response times
✅ Enterprise knowledge retrieval
✅ Real-time human fallback
✅ Voice-enabled customer interaction
✅ Scalable support infrastructure

---

🌍 Use Cases

• SaaS Platforms
• E-commerce Websites
• Enterprise Support Systems
• Educational Platforms
• Healthcare Portals
• Internal Knowledge Assistants

---

🚀 Future Scope

• Multi-language AI support
• CRM integrations
• Advanced analytics dashboard
• AI sentiment analysis
• Autonomous AI workflows
• Enhanced voice intelligence
• Predictive support systems

---

👨‍💻 Team

• Arushi Kadyan
• Pranshu Sharma
• Suryansh Tomar

---

📜 License

This project is developed for academic and research purposes.

---

⭐ Final Note

MultiQuery Pilot is not just a chatbot.

It is a scalable AI-powered interaction infrastructure designed for next-generation customer engagement systems.