As more companies adopt artificial intelligence (AI) for customer service, sales, and internal operations, two issues are moving to the forefront: data control and response speed.
Many agent AI platforms today are built on top of global large language model (LLM) providers. While this allows for rapid deployment, it also raises questions about where enterprise data is processed, how it is stored, and whether companies have full control over their information.
For Gnani Innovations Private Limited, owning the full technology stack is important to its strategy. Founded in 2016, the company develops both the foundation models and the application layer where AI agents are built.
“Few things that I can add why we are different from other agent AI platforms, one is the complete stack. There are multiple layers of the tech stack, which is the foundation model. Then is the application layer, where you build AI agents,” Ananth Nagaraj, co-founder and CTO of Gnani, said during the Trilateral AI and Digital Infrastructure Partnership Ceremony between InfiniVAN Inc. and Gnani Innovations Private Limited, IPS Pro, held recently.
By building and controlling its stack end to end, the company says enterprises can host AI systems in their own data centers, decide where data resides, and define how solutions are deployed. This setup appeals to sectors such as banking, telecom, and government, where data sovereignty and compliance are critical.
As AI adoption deepens across industries, vendors are increasingly differentiating themselves not just on capability, but on how much control, speed, and customization they can offer enterprise customers.
Another concern in enterprise AI is latency. In customer-facing use cases such as voice bots and call centers, delays can disrupt conversations. According to Nagaraj, response time must be almost unnoticeable.
“In our experiences, millions of conversations everywhere, the latency should be subconscious,” he said.
He also noted that a full round trip, including transcription, understanding, and pulling information from back end systems such as CRM platforms, should take less than a second.
To address this, some companies are shifting from very large LLMs to smaller, fine-tuned models tailored for specific enterprise tasks. Gnani deploys small language models in various sizes depending on the use case.
These smaller models offer several advantages. They require less computing power, reduce operating costs, and can be fine-tuned using company-specific data. According to Nagaraj, fine-tuning also reduces hallucinations and allows enterprises to impose stricter guardrails so AI responses stay aligned with company policies.
