Sangameswara, with nearly four decades of experience in technology, transitioned from roles at Intel and Apple to become a serial entrepreneur. His latest innovation, RightSense AI, utilizes large language models like GPT to offer conversational analytics that address the challenges of data literacy within organizations.
Inspired by the underutilization of data in decision-making, Sangam Sangameswara’s vision is to simplify data distillation for individuals within organizations. His platform, RightSense Fusion, utilizes key measurable metrics (KPIs) and enables users to interact with their data in a conversational format with real-time insights. Sangameswara emphasizes the importance of making every organization data-driven.
Sangameswara, founder and CEO of RightSense, came to the U.S. nearly four decades ago to pursue a master's degree in computer science. His early professional years included impressive stints with some of technology’s bigwigs, including a few years with Intel and nearly a decade at Apple during the mid-90s, which provided a strong foundation for ideation, innovation and serial entrepreneurship in various domains.
He learned the importance of navigating diverse cultures. Another key lesson was leveraging cross-functional capabilities for success. He shares that in larger companies, influencing different departments becomes crucial because they often operate independently. These lessons have been invaluable in the success of his startups, where external factors like customer priorities and internal politics needed to be managed and required adaptability and quick decision-making.
Sangameswara’s transition from corporate roles to the startup world began in online advertising before it became a mainstream sector, even before Google/Yahoo. He later was involved in enterprise Saas and video streaming as well. These diverse experiences propelled and prepared him to ideate and innovate RightSense.
Sangameswara took a customer-focused approach in creating RightSense. Sangameswara discusses building a startup with customers as partners, starting with a deep understanding of customer problems. “We ventured into leveraging a GPT kind of a model, what is referred to as Large Language Models (LLMs), to help talk to their data in a casual, but meaningful way. So that is where we are focusing. All the features we want to deliver are in this conversational format. Businesses have tons of data, and with RightSense, they can actually utilize that data effectively for making optimal decisions.”
Sangameswara’s inspiration was the number of businesses with tons of data “cloud data lakes,” being underutilized in decision-making. Sangameswara explains RightSense's approach, “Our platform infers key measurable metrics (KPIs) and allows users to interact with their data through a conversational format.” Sangameswara shares that his vision is to highlight the significance of making data distillation simple and easy for individuals within organizations to make role specific decisions.
Sangameswara elaborates, “Our platform understands a customer’s KPIs. That is the back end of the system. Now, in the front end, customers can simply go in and say, ‘Tell me, what are the sales? Which areas contributed to the sales? Which store contributed? Which product contributed?’” With RightSense, customers can go even further. They can ask why those stores and products didn’t contribute. Even more importantly, they can ask how they can fix the problem.
Sangameswara explains that Google initially developed the core technology behind models like GPT (Generative Pre-trained Transformer), which were very expensive and accessible only to big companies with substantial financial resources.
OpenAI, which created the power of these models with ChatGPT, and other companies with deep experience and capital have changed this by making the technology more accessible, similar to how the Internet became widely available. Now, businesses, regardless of their size, can use this LLM technology for specific purposes related to their industry. This shift has made advanced language models commercially viable for a broader range of applications.
Sangameswara’s solution starts with a deep understanding of his customer problems; he means to help companies use this technology to address the specific challenges they are facing within their industry. In essence, RightSense is about applying advanced language models to solve real-world problems in a more user-friendly and commercially feasible way.
He further adds, “Every organization should become data-driven. Our decisions should be based on data. But when you really get down to it, what percent of users are capable of making data-driven decisions within an organization? There is a big gap because however good your dashboard reporting is, many people within the organization are not equipped to understand data and deepdive properly and identify the things that are actually causing problems. This is the issue with what they call the data literacy gap.”
Just about a year back, when OpenAI made chatGPT publicly available, users started experiencing the power of LLMs, Sangameswara realized the advantage of using these models to bring value to enterprise data and decided to focus on creating personalized narratives for individuals based on their responsibilities within the organization, like a manager responsible for a specific territory, stores, or product line.
The idea was to generate these narratives regularly, such as daily, weekly, or monthly, depending on the case. Initially, they used language models to generate these stories automatically. However, they discovered a gap in the process - people didn't perceive the generated stories as real-time because they were often generated at fixed times, like midnight or the end of the week.
To address this, Sangameswara introduced a real-time conversational element to his RightSense platform. By incorporating real-time data from various sources, the conversation could be in real-time even if the storytelling wasn’t. This meant that when users interacted with the system and asked questions, they received information that reflected the latest available data, similar to having a conversation where you share the most recent known information. Since then, Sangameswara has built prototypes and validated them with current and potential customers. “We are talking to new customers, and the idea is gelling.”
While the RightSense platform and Sangameswara are in a good place, he has much he wants to do. He plans to utilize RightSense to help improve enterprise data literacy and boost organizational productivity by enabling role-specific, function-specific question answering beyond retail and across many domains and industries. “That is at a very high level. But what does it mean? Because productivity means different things to different people and roles. There is a big gap within organizations to make data-driven decision-making optimal. So, we want to continue to help organizations improve.”
To learn more about how RightSense can improve your business, click here.
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