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 …
Marketing Head at AIVeda, a master of impactful marketing strategies. Avinash's expertise in digital marketing and brand positioning ensures AIVeda's innovative AI solutions reach the right audience, driving engagement and business growth.
Enterprises today are overflowing with data. But it’s fragmented. Customer support has audio. Operations has video. Marketing has text. IoT has sensors. You have built great systems, but they do not interact with each other. Multimodal AI breaks those walls. It makes data cooperative. When different data types start speaking to one another, new intelligence …
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 …
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 …
Imagine a global insurance firm. Every month, thousands of claims documents flood in—policies, incident reports, legal assessments. The firm implemented a privately-hosted large-language-model solution so internal teams could query and summarise the data on-premises without ever exposing sensitive customer records to a public cloud model. Within six months, they reduced document-processing time, while preserving full …
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 …
Healthcare has always been about trust. Patients trust you with their most sensitive information—medical histories, lab results, diagnoses, and even the details of their personal lives. If that data leaks or is misused, the damage is permanent. Regulators know this too, which is why healthcare has some of the strictest compliance rules anywhere. At the …
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 …