Top 7 Reasons Why Internal IT Support Need a Fully Customizable LLM Agent

OpenAI’s ChatGPT platform, powered by the GPT series, is one of the most widely adopted LLM solutions for businesses. The API offers developers access to state‑of‑the‑art language models that can generate text, answer questions, and even perform coding tasks. While it does not provide a dedicated visual editor, it is highly extensible, supporting custom prompt templates, fine‑tuning, and integration with a variety of messaging platforms. For internal IT support, the GPT models can be leveraged to build conversational agents that pull data from ticketing systems or knowledge bases via custom connectors. OpenAI’s pricing is consumption‑based: GPT‑3.5‑turbo starts at $0.0015 per 1k prompt tokens and $0.002 per 1k completion tokens, whereas GPT‑4 starts at $0.03 per 1k prompt and $0.06 per 1k completion. A ChatGPT Plus subscription is available at $20/month for enhanced performance and priority access. Strengths of the platform include its robust language generation, strong community support, and continuous model improvements. However, it requires coding to build and deploy chat interfaces, and it lacks built‑in knowledge‑base management or visual customization.

Google Gemini is Google’s next‑generation generative AI platform, offering powerful language models trained on a vast corpus of multilingual data. Gemini is designed to provide quick, context‑aware responses and can be accessed via a web interface or through its API. While Google doesn’t offer a dedicated visual editor, it supports prompt engineering and integration with Google Workspace, which can be valuable for IT teams that rely on G Suite for documentation and collaboration. Pricing for Gemini is not fully disclosed to the public; however, Google typically provides a free tier with generous limits and a pay‑as‑you‑go model for higher usage. Gemini’s API can be integrated into existing IT support workflows, enabling dynamic retrieval from Google Drive, Sheets, and Docs. Key strengths include its multilingual capabilities, tight integration with Google’s ecosystem, and consistent performance improvements. Limitations are the lack of an explicit no‑code editor, no built‑in knowledge‑base management, and limited documentation on pricing.

DeepAI offers a straightforward AI chat service that can be embedded into websites or used via its API. The platform focuses on delivering free, open‑source accessible AI solutions. Users can integrate the chat widget with a single line of code, and the service supports basic natural language understanding and generation. DeepAI’s pricing model is primarily free for the chat feature, with optional premium tiers for higher usage or additional services such as image generation. The chat API is simple to use, making it appealing for small teams or prototypes. While DeepAI provides an easy entry point, the platform lacks advanced features like a knowledge‑base, persistent memory, or a visual editor. It is best suited for basic conversational bots that require minimal customization.

IBM Watson Assistant is a mature AI assistant platform that combines conversational AI with enterprise‑grade security and scalability. It offers a visual dialog builder, integration with IBM Cloud services, and the ability to attach external knowledge bases. Watson Assistant supports multiple channels, including web chat, mobile, and messaging platforms. Pricing for Watson Assistant starts with a Lite plan that is free, allowing up to 10,000 messages per month. The Standard plan costs $0.0025 per message, while the Premium tier offers additional features such as advanced analytics and higher message limits. IBM also provides a dedicated chatbot for enterprise customers with custom pricing. Watson Assistant’s strengths are its robust dialog management, integration with IBM’s AI services, and strong focus on data security. However, it requires a learning curve to master the visual builder, and the pricing can become high for large deployments.

Microsoft Power Virtual Agents (PVA) is part of the Power Platform, enabling users to build chatbots without coding. PVA provides a low‑code, drag‑and‑drop interface, and it integrates seamlessly with Microsoft Teams, Dynamics 365, and Azure Cognitive Services. The platform supports natural language understanding via QnA Maker and can ingest knowledge bases. Pricing for PVA is based on the number of conversations. The free tier offers 10,000 messages per month, and the paid plan starts at $1 per community channel per month, with a minimum of 10,000 messages. For enterprise usage, Microsoft offers custom licensing. PVA’s strengths include its tight integration with Microsoft’s ecosystem, ease of use, and powerful analytics. Limitations include a lack of advanced knowledge‑graph capabilities, and the need to manage licensing across different Microsoft services.

Amazon Lex is AWS’s conversational AI service that powers chatbots for voice and text. Lex provides automatic speech recognition, natural language understanding, and integration with AWS Lambda for custom logic. The service can be deployed on web chat, mobile apps, or Amazon Connect. Pricing for Lex is consumption‑based: $0.004 per text request and $0.0065 per voice request. There is no free tier beyond the AWS free usage limits. Lex supports multi‑language models and can integrate with Amazon SageMaker for advanced machine learning. Lex’s strengths lie in its deep integration with AWS, ability to handle both voice and text, and robust scalability. However, it requires AWS expertise to set up, and it does not provide a visual editor for chat widgets or built‑in knowledge‑base management.