AI Agent - Voice Travel Planner

AI Agent -
Voice Travel Planner

Industry

Team size

3 people

Type

Web App

Location

Asia

Timeline

2 weeks

Technologies

N8n, Pinecone, Elevenlabs, AWS, ChatGPT

Demo Account
Email: test123@yopmail.com Password: 123456@aA

Demo Account
Email: test123@yopmail.com
Password: 123456@aA

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Project Overview

The Voice AI Travel Agent is an intelligent assistant that helps users plan personalized trips through natural voice conversations. By understanding user preferences in real-time, the AI builds a customized travel itinerary including flights, accommodation, dining, and activities. Once the conversation ends, the full travel plan is sent to the user’s email for convenience and booking. This solution combines voice interaction with smart travel planning to enhance user experience and reduce manual search time.

Key Features

Conversational Trip Planning: Plan your trip by simply talking to the AI—no forms or browsing required.
Personalized Itinerary Creation: AI suggests tailored flights, hotels, food, and activities based on user input.
Automated Email Delivery: Sends a complete travel plan to the user’s inbox after the conversation.
Voice-First User Experience: Built for hands-free, intuitive interaction through voice.
Smart Recommendation Engine: Combines travel data with user preferences for highly relevant suggestions.

Challenges

1. Low-Quality Input Documents: Documents are often blurry, skewed, or poorly captured, making it difficult for OCR to accurately recognize text.
2. Variety of Document Formats (Invoices, Delivery Notes, Itemized Lists, etc.): Each logistics partner or client may use different templates, making it difficult for a single model to accurately extract data across all formats.
3. Complex Table Extraction (Tabular Data): Tables containing product or cost information are often misread by OCR, resulting in broken rows or columns and inaccurate data.
4. Imperfect Extracted Data – Needs Verification: AI cannot guarantee 100% accuracy, which can lead to system errors if outputs aren't verified.

Solutions

Apply image pre-processing techniques such as: Sharpening and contrast enhancement , automatic deskewing (straightening the image), use libraries like OpenCV or PIL to standardize images before feeding them into the OCR process.
Develop a template classification model to identify the document type before extraction. Allow customizable training for enterprise-specific document templates.
Utilize specialized models for table extraction, such as Amazon Textract AnalyzeDocument.
Implement a human-in-the-loop confirmation interface that displays AI results and allows quick manual corrections.
Use confidence scores to highlight fields that require user verification.
Log corrected data to retrain and improve the model over time.

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