June 07, 2023

Integrating ChatGPT into corporate conversational systems

Artificial intelligence (AI) and machine learning (ML) are transforming the way companies interact with both their customers and employees. ChatGPT is one of the most interesting solutions for companies seeking to integrate state-of-the-art natural language processing technology into their corporate conversational systems.

Artificial intelligence (AI) and machine learning (ML) are transforming the way companies interact with both their customers and employees. Conversational systems, such as chatbots and virtual assistants, have become an increasingly popular tool to improve user experience and automate interactions.

Some companies, including el corte inglés, IKEA, H&M, Alain Afflelou, Booking, Pizza Hut, Uber, Sephora, and more have been offering a high-quality 24/7 customer service for years via virtual assistants who help with purchases and respond to some of the most common queries. However, most of these assistants, while undoubtedly providing numerous advantages, are limited to answering questions from a specific domain and providing somewhat artificial interaction. At SEIDOR, we have also created several assistants of this type, such as Jetairlines or Queda with Zuri.

In this context, ChatGPT is an interesting solution for companies seeking to integrate state-of-the-art natural language processing (NLP) technology into their corporate conversational systems. With the ability to understand and generate natural language, ChatGPT can help significantly improve the quality of user interactions and experience. This is because the responses provided can encompass multiple domains and can be, in many cases, almost indistinguishable from real human interaction.

To integrate ChatGPT into corporate conversational systems, it is important to clearly define the objectives and scope of the integration: Is the objective to improve the quality of the chatbot's responses? Is the objective to allow the virtual assistant to better understand natural language? Is the objective to increase the efficiency of the system in general?

Relevant data which is representative of the domain in which the system will operate must also be prepared. This will be used to train the model, which requires significant time and resources.

Once the integration has been implemented, it is important to monitor, follow up and maintain the system's performance, as well as making continuous improvements and corrections, incorporating new data sources, and improving the accuracy of the model.

The integration of the Chat GPT language engine is carried out through the OpenAI API programme.


OpenAI offers access to its GPT natural language API through its OpenAI API programme. Using this programme, companies and developers can use this API to integrate the capacity for natural language processing into their own applications and services.

The OpenAI API can be applied to almost any task that involves understanding or generating natural language, code or imagery. It offers different models which are suited to different tasks, as well as the potential to adjust your own customised models. These models can be used for everything from content generation to semantic search and classification.

Some of the most used models include:

  1. GPT-3: This is one of the most powerful and advanced models of the OpenAI API, and can generate consistent and understandable high-quality text for a wide range of tasks.
  2. GPT-2: This is the model prior to GPT-3, and it also very powerful when it comes to generating text, although it is less advanced than GPT-3.
  3. BERT: This is a deep learning model that focuses on understanding the meaning and relationships between words in a given text. It is especially suitable for tasks such as text classification and information extraction.
  4. DALL-E: This is a model that generates images from text descriptions. It can create realistic and detailed images of objects and scenes that have never before existed.
  5. CLIP: This is an open source model that can understand both words and images, and this makes it suitable for tasks such as image classification and keyword image search.

Access to the OpenAI API is available through a subscription payment plan that varies according to the level of use, the model chosen and the number of API requests.

Examples of integration

Examples of the OpenAI API integration that can be found online include:

  • Hugging Face: Hugging Face is an artificial intelligence development platform that offers access to several natural language APIs, including the OpenAI API. Its website offers a variety of examples of code to use the OpenAI API in different applications, including chatbots, text and image generation, sentiment analysis, and much more.
  • GPT-3 Playground: The GPT-3 Playground is an online tool that uses OpenAI API to generate user-based text. Users can enter a word or phrase and watch as GPT-3 generates a continuous text based on that input.
  • OpenAI API Samples: The OpenAI website offers an API sample section that provides examples of code in several programming languages which can be used with the OpenAI API. The samples include examples of chatbots, text completion and translation between languages.


Artificial Intelligence and machine learning are tools that can help you to automate many of your business' most costly processes, for example, interactions with your customers. Integrating ChatGPT into corporate systems requires advanced technical knowledge of programming and natural language processing. If you have a project of this nature in mind and you're looking for advice and the guarantee that comes with working with a team of professionals, don't hesitate to contact us.