Businesses are now expecting quantifiable commercial results rather than being dazzled by AI demonstrations. Large language models (LLMs) have been adopted by numerous organisations throughout the last two years, with the belief that more always equates to better. This assumption actually didn’t hold up on a large scale. As soon as LLMs were used in …
AI and generative technologies are being quickly adopted by businesses to enhance productivity, decision-making, and customer satisfaction. Nonetheless, a lot of leaders believe that large language models (LLM) inevitably produce greater results. Rising inference costs, significant infrastructure requirements, and growing worries about data privacy and compliance are all consequences of this misperception. Performance in real-world …
In 2024, JPMorgan Chase developed an internal generative AI platform called DocLLM to summarise legal documents securely within its private infrastructure. The reason was clear: traditional cloud-hosted models risked exposing confidential client data. Instead of deploying massive, general-purpose models, the bank built smaller, fine-tuned ones tailored for compliance and cost efficiency. This example highlights a …
Artificial Intelligence (AI) has entered a new era where large language models (LLMs) power everything from chatbots and copilots to knowledge retrieval and compliance automation. These massive models, such as GPT-4 or Gemini, have demonstrated groundbreaking capabilities. But their size also creates challenges: they require enormous compute resources, high costs, and specialized infrastructure that most …