Advantages of Using Specialized AI Customer Support Bots vs Developing Your Own GPT Bot

With the rise of OpenAI and open source large language models (LLMs), high quality AI bots have quickly become accessible to companies of all sizes. It no longer takes months or hundreds of thousands of dollars to get started, and many companies are even considering building their own ChatGPT-like bot to incorporate in their customer support stack. 

While building your own bot is certainly possible, in this article we’ll explore the advantages of using specialized customer support AI bots, such as Mava’s, over developing your own GPT bot.

Prompt Engineering

To start with, many customer support bots leverage the power of OpenAI’s GPT-4 model to ensure top quality AI capabilities. Equipped with the latest technology, they guarantee that their language model matches those that you may develop by yourself. 

Working with a powerful model, however, is only the starting point and developing the right prompts is crucial. Customer support companies have invested significant time in optimizing and extensively testing prompts to ensure accuracy and prevent hallucinations. This process ensures that bots understand and address customer queries correctly and asks for more context when required, rather than respond to a question with an answer that might sound sensible, but is actually incorrect. 

in the case of Mava, we have developed prompts to recognize what is a support question, which is relevant in the context of public support. When supporting your users in public forums or Discord channels, messages will be a mix of casual chat, social questions such as “what did you do last weekend”, and actual support questions for the AI to address. Without properly instructing the AI, it will engage in all types of conversations even when irrelevant.

Integrations with support channels

Existing support bots take care of all the technical aspects by offering built-in integrations. Typically support bots will have an out-of-the-box web chat integration and some bots also integrate with social channels such as Discord and Telegram. This means you can seamlessly deploy the AI bot across multiple channels without the hassle of building and maintaining integrations from scratch. This means you can save time and effort, allowing you to focus on delivering exceptional customer support.

Training your AI Bot

It’s important to train your AI bot with specific company information and  documentation so the bot is able to respond, not only with the correct information, but also with an awareness of the context.

In the case of Mava, you can easily train the AI bot with your existing support content, such as a Gitbook, Google Docs, or FAQ on your website. Mava’s AI will soon also be able to learn from conversations in your community, such as admin responses in Discord channels or answers on forums. What’s more, Mava goes the extra mile by rewriting content to enhance the AI's understanding, using some of the principles discussed in this blog post on enhancing your knowledge base for AI.  

Utilizing vector databases

To further improve the AI’s accuracy accuracy, most support platforms incorporate vector databases to extract the most relevant and contextually appropriate content for generating responses. Traditionally, retrieving relevant information from a vast knowledge base can be time-consuming and resource-intensive. However, vector databases enable AI models to efficiently retrieve specific information by leveraging the semantic relationships encoded in the vectors. This saves time and computational resources, allowing for faster response times, improved overall system performance and more accurate responses.

Advanced AI Support Features

In addition to responding to questions, AI also offers other powerful benefits in the context of customer support. For example, it can automatically categorize tickets and add tags to keep track of the most common support topics. 

Moreover, you may decide to use your chatbot in public forums or Discord channels, in which case it’s important that the AI can distinguish different types of questions. Messages will typically be a mix of casual chat, social questions such as “what did you do last weekend”, and actual support questions for the AI to address. It is important to design the right prompts and properly instruct the AI, to make sure it only engages in relevant conversation. 

Before building your GPT bot, it’s useful to consider whether you plan on adding more advanced AI features and whether you have the resources to do so.

Full-Service Customer Support Platform

Considering all the factors involved, you may still conclude that building your own AI bot for customer support is feasible. Keep in mind, however, that there are other advantages that most customer support platforms offer and would take time for any internal team to recreate.

For instance, AI will never be able to address 100% of customer queries, so you will need a mechanism to transfer tickets that can’t be addressed by the AI to your human support team. The team then needs to have a place to easily view, organize and respond to these queries, which is where support platforms typically come in. 

Moreover, customer support platforms will provide insights into key metrics, such as the AI’s and support team performance, as well as what type of questions come up most often and ticket volumes over time. All this information can be used to further optimize and improve customer support and deliver great customer experiences.


While most support platforms and AI bots charge monthly subscription fees, consider the improved quality and time saving compared to building and maintaining your own bots when deciding what makes sense for your business. When it comes to AI bots for customer support, there are a host of advantages using a specialized AI bot, over developing your own. These benefits range from making use of extensively tested prompts, integrations with messaging platforms and getting access to a comprehensive support platform.

Try out Mava’s AI bot for free 


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