Soham Mazumdar is the Co-Founder and CEO of WisdomAI, an organization on the forefront of AI-driven solutions. Prior to founding WisdomAI in 2023, he was Co-Founder and Chief Architect at Rubrik, where he played a key role in scaling the corporate over a 9-year period. Soham previously held engineering leadership roles at Facebook and Google, where he contributed to core search infrastructure and was recognized with the Google Founder’s Award. He also co-founded Tagtile, a mobile loyalty platform acquired by Facebook. With twenty years of experience in software architecture and AI innovation, Soham is a seasoned entrepreneur and technologist based within the San Francisco Bay Area.
WisdomAI is an AI-native business intelligence platform that helps enterprises access real-time, accurate insights by integrating structured and unstructured data through its proprietary “Knowledge Fabric.” The platform powers specialized AI agents that curate data context, answer business questions in natural language, and proactively surface trends or risks—without generating hallucinated content. Unlike traditional BI tools, WisdomAI uses generative AI strictly for query generation, ensuring high accuracy and reliability. It integrates with existing data ecosystems and supports enterprise-grade security, with early adoption by major firms like Cisco and ConocoPhillips.
You co-founded Rubrik and helped scale it into a significant enterprise success. What inspired you to depart in 2023 and construct WisdomAI—and was there a specific moment that clarified this recent direction?
The enterprise data inefficiency problem was staring me right within the face. During my time at Rubrik, I witnessed firsthand how Fortune 500 corporations were drowning in data but ravenous for insights. Even with all of the infrastructure we built, lower than 20% of enterprise users actually had the precise access and know-how to make use of data effectively of their each day work. It was a large, systemic problem that nobody was really solving.
I’m also a builder by nature – you may see it in my path from Google to Tagtile to Rubrik and now WisdomAI. I get energized by taking up fundamental challenges and constructing solutions from the bottom up. After helping scale Rubrik to enterprise success, I felt that entrepreneurial pull again to tackle something equally ambitious.
Last but not least, the AI opportunity was unimaginable to disregard. By 2023, it became clear that AI could finally bridge that gap between data availability and data usability. The timing felt perfect to construct something that would democratize data insights for each enterprise user, not only the technical few.
The moment of clarity got here when I noticed we could mix all the pieces I’d learned about enterprise data infrastructure at Rubrik with the transformative potential of AI to unravel this fundamental inefficiency problem.
WisdomAI introduces a “Knowledge Fabric” and a collection of AI agents. Are you able to break down how this method works together to maneuver beyond traditional BI dashboards?
We have built an agentic data insights platform that works with data where it’s – structured, unstructured, and even “dirty” data. Fairly than asking analytics teams to run reports, business managers can directly ask questions and drill into details. Our platform could be trained on any data warehousing system by analyzing query logs.
We’re compatible with major cloud data services like Snowflake, Microsoft Fabric, Google’s BigQuery, Amazon’s Redshift, Databricks, and Postgres and in addition just document formats like excel, PDF, powerpoint etc.
Unlike conventional tools designed primarily for analysts, our conversational interface empowers business users to get answers directly, while our multi-agent architecture enables complex queries across diverse data systems.
You’ve got emphasized that WisdomAI avoids hallucinations by separating GenAI from answer generation. Are you able to explain how your system uses GenAI in another way—and why that matters for enterprise trust?
Our AI-Ready Context Model trains on the organization’s data to create a universal context understanding that answers questions with high semantic accuracy while maintaining data privacy and governance. Moreover, we use generative AI to formulate well-scoped queries that allow us to extract data from the several systems, versus feeding raw data into the LLMs. That is crucial for addressing hallucination and safety concerns with LLMs.
You coined the term “Agentic Data Insights Platform.” How is agentic intelligence different from traditional analytics tools and even standard LLM-based assistants?
Traditional BI stacks slow decision-making because every query has to fight its way through disconnected data silos and a relay team of specialists. When a chief revenue officer must know learn how to close the quarter, the reply typically passes through half a dozen hands—analysts wrangling CRM extracts, data engineers stitching files together, and dashboard builders refreshing reports—turning an easy query right into a multi-day project.
Our platform breaks down those silos and puts the complete depth of knowledge one keystroke away, so the CRO can drill from headline metrics all of the strategy to row-level detail in seconds.
No waiting within the analyst queue, no predefined dashboards that may’t sustain with recent questions—just true self-service insights delivered on the speed the business moves.
How do you ensure WisdomAI adapts to the unique data vocabulary and structure of every enterprise? What role does human input play in refining the Knowledge Fabric?
Working with data where and the way it’s – that is essentially the holy grail for enterprise business intelligence. Traditional systems aren’t built to handle unstructured data or “dirty” data with typos and errors. When information exists across varied sources – databases, documents, telemetry data – organizations struggle to integrate this information cohesively.
Without capabilities to handle these diverse data types, invaluable context stays isolated in separate systems. Our platform could be trained on any data warehousing system by analyzing query logs, allowing it to adapt to every organization’s unique data vocabulary and structure.
You’ve got described WisdomAI’s development process as ‘vibe coding’—constructing product experiences directly in code first, then iterating through real-world use. What benefits has this approach given you in comparison with traditional product design?
“Vibe coding” is a major shift in how software is built where developers leverage the ability of AI tools to generate code just by describing the specified functionality in natural language. It’s like an intelligent assistant that does what you would like the software to do, and it writes the code for you. This dramatically reduces the manual time and effort traditionally required for coding.
For years, the creation of digital products has largely followed a well-recognized script: meticulously plan the product and UX design, then execute the event, and iterate based on feedback. The logic was clear because investing in design upfront minimizes costly rework throughout the dearer and time-consuming development phase. But what happens when the price and time to execute that development drastically shrinks? This capability flips the normal development sequence on its head. Suddenly, developers can start constructing functional software based on a high-level understanding of the necessities, even before detailed product and UX designs are finalized.
With the speed of AI code generation, the trouble involved in creating exhaustive upfront designs can, in certain contexts, turn out to be relatively more time-consuming than getting a basic, functional version of the software up and running. The brand new paradigm on this planet of vibe coding becomes: execute (code with AI), then adapt (design and refine).
This approach allows for incredibly early user validation of the core concepts. Imagine getting feedback on the actual functionality of a feature before investing heavily in detailed visual designs. This could result in more user-centric designs, because the design process is directly informed by how users interact with a tangible product.
At WisdomAI, we actively embrace AI code generation. We have found that by embracing rapid initial development, we will quickly test core functionalities and gather invaluable user feedback early in the method, live to tell the tale the product. This permits our design team to then deal with refining the user experience and visual design based on real-world usage, resulting in simpler and user-loved products, faster.
From sales and marketing to manufacturing and customer success, WisdomAI targets a large spectrum of business use cases. Which verticals have seen the fastest adoption—and what use cases have surprised you of their impact?
We have seen transformative results with multiple customers. For F500 oil and gas company, ConocoPhillips, drilling engineers and operators now use our platform to question complex well data directly in natural language. Before WisdomAI, these engineers needed technical help for even basic operational questions on well status or job performance. Now they will immediately access this information while concurrently comparing against best practices of their drilling manuals—all through the identical conversational interface. They evaluated quite a few AI vendors in a six-month process, and our solution delivered a 50% accuracy improvement over the closest competitor.
At a hyper growth Cyber Security company Descope, WisdomAI is used as a virtual data analyst for Sales and Finance. We reduced report creation time from 2-3 days to only 2-3 hours—a 90% decrease. This transformed their weekly sales meetings from data-gathering exercises to strategy sessions focused on actionable insights. As their CRO notes, “Wisdom AI brings data to my fingertips. It really democratizes the info, bringing me the ability to go answer questions and move on with my day, somewhat than define your query, wait for any person to construct that answer, after which get it in 5 days.” This ability to make data-driven decisions with unprecedented speed has been particularly crucial for a fast-growing company within the competitive identity management market.
A practical example: A chief revenue officer asks, “How am I going to shut my quarter?” Our platform immediately offers a listing of pending deals to deal with, together with information on what’s delaying each – reminiscent of specific questions customers are waiting to have answered. This happens with five keystrokes as a substitute of 5 specialists and days of delay.
Many corporations today are overloaded with dashboards, reports, and siloed tools. What are probably the most common misconceptions enterprises have about business intelligence today?
Organizations sit on troves of data yet struggle to leverage this data for quick decision-making. The challenge is not just about having data, but working with it in its natural state – which frequently includes “dirty” data not cleaned of typos or errors. Corporations invest heavily in infrastructure but face bottlenecks with rigid dashboards, poor data hygiene, and siloed information. Most enterprises need specialized teams to run reports, creating significant delays when business leaders need answers quickly. The interface where people devour data stays outdated despite advancements in cloud data engines and data science.
Do you view WisdomAI as augmenting or eventually replacing existing BI tools like Tableau or Looker? How do you fit into the broader enterprise data stack?
We’re compatible with major cloud data services like Snowflake, Microsoft Fabric, Google’s BigQuery, Amazon’s Redshift, Databricks, and Postgres and in addition just document formats like excel, PDF, powerpoint etc. Our approach transforms the interface where people devour data, which has remained outdated despite advancements in cloud data engines and data science.
Looking ahead, where do you see WisdomAI in five years—and the way do you see the concept of “agentic intelligence” evolving across the enterprise landscape?
The long run of analytics is moving from specialist-driven reports to self-service intelligence accessible to everyone. BI tools have been around for 20+ years, but adoption hasn’t even reached 20% of company employees. Meanwhile, in only twelve months, 60% of workplace users adopted ChatGPT, many using it for data evaluation. This dramatic difference shows the potential for conversational interfaces to extend adoption.
We’re seeing a fundamental shift where all employees can directly interrogate data without technical skills. The long run will mix the computational power of AI with natural human interaction, allowing insights to search out users proactively somewhat than requiring them to hunt through dashboards.
