Creating your own Corporate GPT
Who needs a special GPT tailored to their needs and how to implement it?
A so-called “own ChatGPT” is a customized language model based on the GPT architecture. It offers companies the opportunity to use the advanced technology of natural language processing directly for their specific needs. This article provides an introduction to enterprise GPTs: What is it? Who needs one? And what can it look like in practice?
What is a Corporate GPT?
ChatGPT is specifically known as an interactive chatbot based on the GPT architecture. It represents a clear, specialized application of GPT technology that is easily accessible and directly usable.
When people talk about ‘their own Corporate GPT’, they are usually referring to a GPT solution that is specifically tailored to their business needs.
💡 A private GPT is a customized language model that is specifically tailored to the needs and data of a company. By training it with internal data, it can answer employees’ questions efficiently. Full control over the model and the data also enables maximum data security and data protection.
What are examples of Corporate GPTs in practice?
Below we look at some examples of how different organizations use their own GPTs to meet their individual needs:
- A manufacturing company uses a private GPT to increase employee productivity through an internal AI helpdesk. The AI provides answers based on information from operating instructions for large machines, work schedules and order data. For example: “A warning signal lights up on the machine. What does that mean and what do I need to do?”
- An Austrian insurance company uses a private GPT to get quicker answers to questions about regulations, laws and internal guidelines. For example: “Is incident XY insured? To what extent?” The GPT therefore serves as a kind of digital assistant for customer service.
- A government-related company uses a private GPT to retain internal knowledge and process it in the course of a wave of retirements. For example: “My employee XY is retiring in 6 months. What information about her activities is not yet documented?”
When should companies implement a Corporate GPT?
Implementing a Corporate GPT can be beneficial for several reasons. Here are some of the most important aspects that could influence this decision:
Company specific knowledge: Companies can feed their own Corporate GPT with company-specific data and information, allowing the model to develop a deeper understanding of internal processes, products and services. This leads to better support for employees and customers.
Customized answers: A custom GPT solution can also be trained and fine-tuned to cover specific areas of expertise or industries. This makes it possible to provide more precise and relevant answers for specific applications, such as in medicine, law or engineering.
Privacy and control over data: With a dedicated ChatGPT, companies retain full control over their data. This is particularly important in areas where confidential information is processed. Companies can ensure that their data is not viewed or used by third parties.
Data protection: By using their own model, companies can ensure that all data protection guidelines and legal requirements are adhered to, which may not always be guaranteed with external providers.
Internal education and training: The development and use of a private GPT promotes internal understanding and competence in the use of modern AI technologies. This can strengthen the culture of innovation within the company.
Consistency: A company’s own GPT solution can be flexibly customized and evolved to keep pace with the changing requirements and needs of the business. Companies are not dependent on the updates and changes of an external provider.
Independence from third-party providers: An private model gives companies more independence from external providers and their terms and conditions. This can be particularly advantageous if there are changes in the providers’ terms of service or pricing models.
Advanced functions: A custom ChatGPT offers the opportunity to implement experimental features and functions that may not be available in standard solutions. This can lead to innovative applications and competitive advantages.
Long-term cost savings: While the initial cost of developing and implementing your own ChatGPT can be high, it can be more cost-effective in the long term, especially if the model is used intensively. This can reduce reliance on paid external API access.
How to create your Corporate GPT?
Creating your own GPT model requires several steps that include technical know-how and appropriate resources. Typically, the skills for this process are not fully available in-house, but professional GPT providers are called in.
Here is an overview of the most important steps:
- Requirements analysis and planning : First, the objectives are defined and the available technical resources, including hardware, data and technical personnel, are evaluated.
- Data acquisition and preparation : Relevant data sets must be collected, cleaned and formatted to ensure high quality and consistency.
- Model selection and training: A suitable GPT architecture is selected, pre-trained models are used and adapted to the requirements with specific data.
- Setting up infrastructure : Necessary computing power is provided and required software and libraries such as TensorFlow or PyTorch are installed.
- Model training and validation : The model is trained on the collected data, the process is monitored and optimized, and the performance is tested on a separate dataset.
- Implementation and integration : The trained model is implemented into the system environment and integrated into existing applications and workflows.
- Maintenance and development : Model performance is continuously monitored and the model is regularly updated with new data and optimizations.
💡 For a sustainable approach, consider quality-assured platforms that focuses on the continuous improvement of the data, instead of a one-off clean up. There are also software manufacturers and consultants who support the implementation of a private GPT — our Partner ONTEC, for example 😉
What alternatives are there to your own GPT model?
Training your own model can often be a very useful, but possibly costly undertaking. Are there any alternatives?
This leads us to RAGs. At this point, we will not go into the technology in detail, but we will briefly discuss the differences.
💡 In contrast to a private GPT, a RAG is not specifically trained with the data, but uses an LLM (an already trained standard GPT model) to query completely different data sources.
In the course of this query, a RAG also has the option of running different tests so that the resulting answers are as helpful as possible.
A RAG therefore has several advantages over a GPT:
- Training a GPT takes days, weeks or months
- The information in an internal GPT can therefore not simply be kept up to date on a daily basis.
- The GPT can no longer forget data with which it has been trained. Once you have trained it, you can’t get it out again. If you are dissatisfied with the data basis, you have to start all over again.
- A GPT without RAG supplementation can hallucinate without you realizing it. In fact, hallucinations of an private GPT sound particularly realistic because it can use the internal language.
- A GPT can fall victim to a prompt injection attack – but with underlying RAG this risk is minimized.
Conclusion & Key Takeaways
The decision to develop a company’s own GPT brings numerous benefits, from customized solutions and increased data security to cost efficiency and innovation potential.
However, the initial investment and need for specialized resources must be carefully weighed. Companies that are prepared to take on these challenges can benefit significantly from the long-term advantages of an in-house GPT solution.
- A private GPT enables tailored solutions, greater data security and improved relevance of the responses generated through specific customization of the business.
- Possible applications include personalized customer advice, internal knowledge management, support for medical diagnoses and the automation of routine tasks.
- Companies should ideally clarify with their trusted AI developers / consultants whether creating their own ChatGPT, a RAG solution or another system is the ideal solution.
About the author:
Tobias Eljasik-Swoboda is an AI Architect at ONTEC, a company that creates IT-solutions for business-critical processes. Having developed manifold AI solutions for companies of different industries, his focus nowadays is building reliable RAG-systems that are tailored to the company’s specific needs. Beyond the pure creation of AI solution, he also acts as an AI advisor in the fields of explainable AI research and reliable AI solutions.
