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 …
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 …
Enterprises today are no longer asking if they should adopt generative AI — they are asking how to adopt it safely and strategically. Large Language Models (LLMs) are powering everything from intelligent agents and search to knowledge management and automated documentation. But for many organizations — particularly in healthcare, banking, pharma, defense and manufacturing — …
The rapid growth of generative AI has redefined how enterprises handle customer engagement, automate processes, and extract value from data. Yet, as businesses rush to integrate large language models (LLMs) into their workflows, a critical question arises: where should these models be deployed? Public LLM APIs like OpenAI or Anthropic offer agility, but they introduce …
If you’re a fintech founder, CFO at a growing company, or managing financial operations at any level, you’ve probably noticed something: the old ways of doing finance just aren’t cutting it anymore. Manual processes that once seemed “good enough” are now bottlenecks. Reports that took days to compile are needed in hours. Customer expectations have …