Large language models have advanced quickly from experimentation to boardroom discussions. However, many businesses continue to have difficulty going beyond pilots. The explanation is simple: AI was built for consumers, not for businesses that handle sensitive data, regulatory exposure, and complex systems. There are significant risks associated with public AI technologies. They put businesses at …
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 …
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Large language models are becoming an important part of how modern businesses operate. Companies now use them for customer support, internal knowledge access, reporting, and decision-making. As this adoption grows, businesses are also becoming more cautious about how their data is processed and protected. This makes choosing the right private LLM provider a critical decision …
Generative AI is rapidly reshaping how modern companies operate, but it’s also exposing serious vulnerabilities for enterprises that rely on public AI platforms. Today, organizations need more than just powerful AI; they need secure, compliant, and fully controlled systems. It’s no surprise that over 27% of organizations have already restricted the use of public GenAI …
Businesses are rushing to incorporate AI into processes, but the more they investigate generative models, the more it becomes evident that control, governance, and security are just as important as model accuracy. Sensitive data cannot be handled by public APIs; government agencies, manufacturing, healthcare, insurance, and finance all need greater control over data, model behaviour, …
Artificial intelligence is no longer a futuristic idea but a priority for every company. Furthermore, as per the Marketsandmarkets recent report, the artificial intelligence market is growing at an astonishing pace, and is expected to hit USD 2,407 billion by the end of 2032. By looking at the stats, it’s not wrong to say that …
Public LLMs helped enterprises understand what generative AI can do. They boosted productivity and made complex tasks easier. But they also exposed a critical flaw. These models sit outside the enterprise boundary. They run on shared infrastructure and retain data unless configured otherwise. Over 27% of organizations restricted the use of public GenAI tools because …
When a retail chain predicts store demand before stock runs out, or a hospital’s digital assistant alerts doctors to potential patient risks in real time, it’s not just AI at work. But it’s AI working together. Yet most enterprises still run AI tools in silos, like chatbots, analytics, recommendation engines, each powerful but disconnected. Decisions …
Banks, insurers, payment firms—your industry (BFSI: Banking, Financial Services, Insurance) sits under intense pressure. Customers expect fast, smart, personalized service. Regulators enforce heavy rules. Fraudsters and cyber threats never sleep. When you add in the promise (and risk) of AI, especially large language models (LLMs), you’ve got to get security and compliance right. Private LLMs …
