Generative AI Framework

Use the power of AI to make the data even smarter

Companies need to identify the use cases that drive competitive advantage

Generative AI takes it a step further by using data to create content that’s entirely new and in various formats. Using machine learning models, generative AI produces original content based on patterns learned from existing data.

1

Framework Implemented and 2 closing

“Our framework establish a common approach across use cases”

  • Re-use generic components
  • Manage shared resources (LLM,DB, Services)
  • Monitor usage and costs
  • Reduce effort on duplicate
  • Functions across use cases
  • Prepare for future scaling
  • Devoteam accelerator based on experience

How does Gen AI works?

Generative AI uses machine learning to analyze common patterns and arrangements in large sets of data, and then uses this information to generate new content similar to the existing data it’s trained on. The more data or examples generative AI has to learn from, the more sophisticated it becomes.

Strengthen competitive position with truly distinctive use cases that both drive value and are challenging to adopt.

Tailored to the specific needs and challenges of a particular organization. They often involve developing custom generative AI solutions.

There is low barrier to adopting use cases that rely on existing LLM applications, these will be important to keep pace with other organizations

Why Gen AI is important?

While many continue to view generative AI with a degree of healthy skepticism, there’s no question that it has the potential to do extraordinary things. 

Generative models can simulate real-world scenarios, and this makes for a highly valuable tool for scientific research, engineering, and risk assessment. It can also suggest novel molecular structures, simulate protein folding and other complex biological processes to aid medical and scientific advancements.