Private GenAI
Generative AI is the biggest revolution in the technology world since the personal PCs
The quote is not just our words, and many qualified voices in the technology, such as Elon Musk and Bill Gates, recognized the power of GenAI and the impact that it will have for business but also in our private lives. GenAI has a comparable impact to technology like Google and search engines in the beginning of the nineties. These tools changed the way in which we access and search for information, and exactly GenAI is doing the same.
GenAI is a creative force that enables us to create content in an easy way, it can help with the creation of texts, but it is also it can search information online, summarize very long documents, learn immediately from files that we provide and create different kinds of multimedia files.
Companies and private individuals have to prepare to get into this new revolution, the option is to jump into this train now or later, but GenAI already changed the world.
When we speak about GenAI, ChatGPT is the reference that comes to our head. And most of us have use it at some moment. With a simple interface that works in most of the devices, it is a reference that can help us to write essays, answer complicated questions, or foster our creativity.
Indeed, the power of ChatGPT makes it a reference, but we can think about ChatGPT as a public GenAI method, and what does it exactly mean?
With ChatGPT we send our data to a web site, and this data is used for other purposes such as retraining. And if we are speaking about public GenAI tools, it is because there is an alternative with with private GenAI tools, and it makes sense to understand the differences.
A new approach to GenAI
So, this is to say that the business model of ChatGPT where we send all our information to a third party and the information that we requests comes back to us is not the only way.
OpenAI, the creator of ChatGPT, was pushing, from a Marketing perspective, to promote bigger models, which only the internet mammoths can host, so in that sense, ChatGPT would not have any competition, because they are a necessity that cannot be replicated.
However, the GenAI race is a really exciting one in many senses, and many companies like Meta, Google, Microsoft, Alibaba and many others tried to create to create their own Large Language Models (LLM), which is a way in which we can think of “their own versions of ChatGPT”. The ecosystem became very dynamic and companies like HuggingFace have rankings to compare the different LLMs, and the output is that the bigger does not necessarily mean the best. In a weekly basis, the rankings change, and with such dynamic technology, we can say that the newer algorithms are superior and also the use case is a determining factor when selecting the most appropriate LLM. It is not the same to create a translation from Chinese to Croatian or to troubleshoot a javascript code.
Those other LLM versions can be Open Source or proprietary, and with Open Source, it is something that anyone can install them and use them as you would like, like for example to make a business around it.
The discussion about proprietary versus Open Source, is something that is being discussed extremely in the net, and as mentioned before, proprietary does not mean better. But a new discussion that is not so widely discussed yet is about the private LLMs.
The concept of a private LLM is simply that a LLM model, that is Opensource, is installed in a machine which is fully reserved to me. So the data is not sent anywhere and I can customize how the system works.
How we can help
In Lean-Link as part of the AI community, we are fully devoted to create solutions that can help anyone. We also offer our hosted private LLMs, where we have a 100% commitment that the data is owned by you and you have full control.
Our Innovations
Industry Focused Use cases
Secure document translation
Many organizations handle sensitive documents that contain confidential information. Private LLMs can be used to develop secure document translation services, allowing these organizations to translate complex documents without exposing the content to external parties. This ensures the privacy and security of the information while still enabling effective communication.
Confidential customer support
Businesses that handle sensitive customer information, such as financial institutions or healthcare providers, can use private LLMs to develop virtual customer support agents. These agents can provide personalized assistance and answers to customer inquiries while keeping all data within the organization’s secure infrastructure, ensuring the privacy and security of the customer’s information.
Personalized research assistance
Researchers and professionals often need to search through vast amounts of documents and data to find relevant information. Private LLMs can be used to create Retrieval Augmented Generation (RAG) systems that can quickly and accurately search through internal databases and provide tailored answers, without the risk of exposing sensitive data to external search engines or cloud-based services.
Secure knowledge management
Organizations often need to maintain and share internal knowledge and expertise. Private LLMs can be used to create knowledge management systems that can extract insights and recommendations from a company’s internal documents and databases, without the risk of exposing this information to external parties. This allows for the effective sharing of knowledge while maintaining the confidentiality of sensitive information.