How I Built A Simple ‘BPO’ Company, All AI Employees (All Local)
Tags: rag, llm, ai, machinelearning
Published: 2024-05-25 15:41:46

Disrupting the BPO Industry: My Journey Building a Fully Automated Company with AI Employees

[Full Article](https://medium.com/@learn-simplified/how-i-built-a-simple-bpo-company-all-ai-employees-all-local-631e48fa908a)

● What Are We Doing Today?
We are building a BPO (Business Process Outsourcing) call center for an imaginary electric company called "Aniket Very General Electric Company". We will create different departments staffed by AI agents who can chat (and eventually speak in next part) with customers to answer questions, handle complaints, or provide services.

● Why Should You Read This Article?
Learning how to build AI agents that can do tasks in real setting, co ordinate w/ human, AI, providing technical support will be a highly valuable skill.

● How Are We Going to Build Our All AI Employees Company?
○ We will explain what BPO and call centers are.
○ Our AI company will have departments like Customer Service, Tech Support, Billing & Payments, Outage Management, and Onboarding Customers.
○ We will use Docker containers to run the Dify AI platform as the base.
○ The AI agents will use the LLaMA-3 language model from Meta AI.
○ We may use Groq's AI accelerator chip to make LLaMA-3 faster.
○ Each department will have a knowledge base of text files that the AI agents can reference.

● Let's Get Cooking!
This section provides setup instructions for installing Docker, Ollama (for running LLaMA-3), and the Dify AI platform. It also outlines the different AI agents we will create for departments like Reception, Customer Service, Billing, Tech Support, etc.

● Let's Design our Organization
○ We explain how each department's AI agents will have their own knowledge base, like an employee handbook.
○ The knowledge bases will contain policies, procedures, and other key information.
○ The AI agents can quickly reference this information to provide accurate and knowledgeable responses.

● Let's Meet Our AI Employees
○ We chose the LLaMA-3 70B model as the base for all AI agents across departments.

○ We give the AI agents customized prompts to define their personalities and roles. ○ The knowledge bases act as training materials tailored to each department.

○ In the future, AI agents could have additional tools like ticket systems and integrations.

● Let's Run Our BPO Organization

Now that the AI workforce and knowledge bases are ready, we can open our BPO company and have the AI agents start handling customer inquiries across different departments like billing, tech support, outages, and new connections.

● Debugging

This section highlights the importance of debugging, showing traces of how the language model understands customer queries and retrieves relevant context from knowledge bases to provide good responses.

● Future Work
○ Scale up to handle more customers using cloud services or distributed computing.
○ Move AI agents and knowledge bases to the cloud for accessibility and maintenance.
○ Fine-tune language models for better performance in each department. ○ Use scalable vector databases for faster knowledge retrieval.
○ Enable voice interfaces and computer vision for more natural interactions. ○ Implement continuous learning so AI agents can expand their knowledge over time.

The article demonstrates the potential of building an actual AI-powered company and raises thought-provoking questions about the role of humans, ethics, and using AI to create a better world.