AI Agent - Customer Support

AI Agent -
Customer Support

Industry

Team size

3 people

Type

Web App

Location

Netherlands

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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AI Agent - Customer Support Screens

Project Overview

This project automates an intelligent FAQ support system by integrating n8n, Pinecone, and an AI agent. It allows users to ask questions related to the system and receive accurate, real-time responses by searching a vectorized knowledge base. page.

Key Features

Knowledge Storage with Pinecone: All relevant system documentation and information are embedded and stored in Pinecone as vector data for efficient semantic search.
Webhook Endpoint for User Questions: A public n8n webhook is created to receive user-submitted questions from the system's Q&A page.
AI-Powered Semantic Search: When a question is received, it is passed to an AI Agent that searches Pinecone to find the most relevant answer based on semantic similarity.
AI Voice Call via ElevenLabs: Once a phone number is received, ElevenLabs is used to place a call to the customer and schedule the meeting.
Automated Response Delivery: The system automatically sends the appropriate answer back to the user through the webhook response.
Live Integration on Website Q&A Page: The entire system is implemented and accessible from the Q&A section of the website, providing an interactive and intelligent help experience

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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