About Agetix
Sovereign AI
Specialised Language Models
Computational Platforms
Research
Agetix. is a research and development project that focuses at enabling AI for all, focus at Agentic AI, Augmentation with AI, Training AI models on proprietery and condiential data in a secure and ethical manner. We are building a team researchers, developers and engineers that are passionate about the potential of AI and its benefit for our society. Our immediate goal is to have all companies starting to use Gen AI capabilities, so in long term they maintain their competitiveness. To achieve this, we are building an AI Starter Package. We strongly believe that is he best interest of any organisation to harness the progress made by the Open Source AI Technologies and LLM's, instead of putting their business data on a closed Gen AI systems. In medium term we are focusing at developing Industry Specific Template Constructs that can be used by organisation to get tangible benefits from AI in general and Gen AI in particular. Our main goal, however, is researching ways of improving the current AI state by finding new applications for LLM’s and harvest new benefits such as efficiency, safety, democratisation, but also develop and leverage of Gen AI for nonhuman communication. If you want to know more write us at admin@agetix.com
Problem. Every company has built over time their industry experience and intellectual property that enabled them to succeed, using acquired knowledge and research spanning across decades or even more. By feeding all that intellectual capital into a Public Closed Gen AI system, it will enable training on proprietary data. It is likely that over time the AI System will learn the industrial processes and blueprints to such a depth that it will allow anyone to produce that device or part by only connecting a production line (fabricator) to a Gen AI system. There is a very high change that Gen AI systems will create fabrication templates that can be used to produce products that are not only a like only, but better than the original blueprinnts used for training. Furthermore, it is not recommended to feed confidential data into Public AI Systems.
Solution. By feeding all that intellectual capital into a Public Closed AI system, it will allow AI’s to be trained on proprietary data will enable the AI to learn and ultimately allow that learning to be leveraged competitors.
Value. It is likely that over time the AI will learn the industrial processes and blueprints to such a depth that will allow anyone to produce that device or part by only connecting a production line to the AI. There is a very high change that Gen AI systems will create fabrication templates and use them to produce products that are not only similar but better than those that were initially used for the AI learning process.
Problem. Companies collect data that is relevant to their businesses and industry. There are various industrial processes where data is collected. A General Language Model, may not be the most efficient way of using that data and transforming it into a business advantage as that data may be specific to an industry or a use case.
Solution. With Specialised Language Models or Small Language Models we are aiming to be better then the standard at specific tasks for an industry or use cases.
Value. Training the Specialised LLM’s will give a competitive advantage to those undertaking this challenge.
Problem. Modern LLM’s are primarily used as chat bots (E.g. Chat GPT of Open AI). This is a very good use case, but long term, there may be other use case of the LLM’s such as computational platform for applications which are AI enabled.
Solution. Use LLM’s as a computational platform for applications. To transform an LLM into a platform would be required to consider various aspects such as tenancy, authentication, scale, monetisation, moderation etc.
Value. This can be new model for General Computing, with a high potential of replacing the “serverless” applications or even SaaS applications.
Identify ways of improving the current AI state by finding new applications for Large Language Models and harvest new benefits such as efficiency, safety, democratisation, but also develop and leverage of Gen AI for nonhuman communication.