AI-Powered Order Matching

The FTT platform leverages advanced AI technologies, open-source models, and distributed computing architecture to help clients automate and enhance the sales lead matching process across multiple social media channels. It collects potential customer contact information and facilitates order matchmaking. Below is the updated technical documentation, incorporating your actual technology stack (such as Ollama, Hugging Face, RAG system, knowledge graphs, LLM large language models, deep convolutional neural networks, large language vision models, and Agent Grid technology).

1. User Profile Creation

The first step in the matching process is to create a detailed user profile for the client. The client's business information, product details, and target market form the foundation of the profile. This information helps the AI system understand the client's needs and supports the subsequent RAG (retrieval-augmented generation) system, ensuring efficient matching with the target audience.

2. Target Audience Definition

Based on the client's user profile, the system analyzes the target market and defines specific characteristics such as industry, geographical location, company size, role, and purchasing behavior. These characteristics are integrated into the knowledge graph for system building.

3. Social Media Channel Scanning and Data Collection

Once the target audience profile and knowledge graph are defined, the AI system begins the search process across major social media platforms. The system combines Hugging Face open-source models, LLM large language models, and RAG technology to use natural language processing (NLP) and knowledge graphs to analyze social media content and identify relevant information about potential customers.

• LLM Large Language Models and Knowledge Graphs: The AI parses the textual data on social media using LLM models, understanding the needs and intent of potential customers. Through knowledge graphs, the AI further infers business relationships, demands, and industry trends, providing more precise matching.

• Deep Convolutional Neural Networks (CNN) and Large Language Vision Models: For posts containing image content, the AI uses deep convolutional neural networks (CNN) for image recognition and combines large language vision models to semantically analyze the content of the images. This enables the platform to identify and understand key information in the images, accurately judging their relevance to the client's needs.

4. Potential Customer Contact Information Identification

During the social media scanning process, the AI system intelligently identifies the contact information of potential customers. These contact details will be used for subsequent bulk outreach.

5. Bulk Outreach and Personalized Communication

Once the contact information is collected, the AI system initiates bulk outreach. The outreach process includes:

• Email Marketing and Personalized Content Generation: Using the collected email addresses, the AI generates personalized emails, tailoring the content based on the target customer's profile and needs.

• Social Media Direct Messages: The AI also sends customized private messages via social media platforms to initiate contact.

• SMS or Other Channels: If phone numbers are available, the AI can reach out via SMS or other communication channels.

All outreach content is generated based on the client profile and potential customer behavioral data to ensure relevance and effectiveness.