Sreekanth (Sree) Mallikarjun Of AI at Reorg: How We Leveraged AI To Take Our Company To The Next Level
An Interview With Chad Silverstein
CreditAI by Reorg™: We launched CreditAI by Reorg, our proprietary, interactive GenAI tool, last fall. Since its launch in October, CreditAI has transformed the way our customers access and distill credit information. The goal of this tool is to simplify the complexity of the data presented to our customers, providing direct access to our library of over 11,000 companies globally, dating back to 2018. To date, over 10,000 clients’ credit-related questions have been answered by CreditAI.
In the ever-evolving and never-ending landscape of business, staying ahead of the curve is a prerequisite for success. Artificial Intelligence (AI) has gone from being a futuristic concept to a daily business tool that executives can’t ignore. In this interview series, we would like to talk with business leaders who’ve successfully integrated A.I. into their operations, transforming their companies in the process. I had the pleasure of interviewing Sreekanth (Sree) Mallikarjun, Chief Scientist and Head of AI at Reorg.
Sree leads AI innovation at Reorg, driving breakthroughs in Generative AI, Machine Learning, Natural Language Processing and Statistics. His team’s work transforms how Reorg’s vast credit datasets are accessed, delivering unprecedented speed and accuracy to both employees and subscribers. With a Ph.D. in Technology and Innovation, Sree’s expertise elevates Reorg’s data capabilities. As an adjunct professor at the University of Virginia School of Data Science, he continues to shape the future of AI while keeping Reorg at the leading edge of financial intelligence.
Thank you so much for doing this with us! To set the stage, tell us briefly about your childhood and background.
My childhood interest in mechanics and robotics led me to study physics and later I pursued mechanical engineering, specializing in robotics, in college. However, I realized that the applied mathematics behind the scenes was the common denominator behind my interest in the field. By digging deeper into mathematics, I got to a point where I was reconciling statistical learning with operations research and found the beauty between them, which led to my career in Data Science.
Today, I lead Reorg’s AI efforts, where I focus on applications of GenAI, ML, and NLP. My journey at Reorg began in 2015, just around the time that data science was beginning to gain traction in the finance sector. Since joining, I have led efforts to find innovative solutions to effectively analyze and produce content for our subscribers, while also empowering my team to work more efficiently overall. This is essential for our organization and customers as they rely on accuracy and speed to navigate a multitude of credit situations and make decisions that impact millions, if not billions, of dollars in securities every day.
What were the early challenges you faced in your career, and how did they shape your approach to leadership?
As a budding data scientist, the early days were challenging, especially when it came to finding training data to build models and understanding how to balance false positives and false negatives. I was the first and only data scientist at Reorg for almost three years. Aligning with the business and complex problems that we were dealing with was the hardest part. Getting the business side to see how these problems could be solved through my data-driven perspective took some time. Being a small and fast-moving company, many were often too busy to pause and train a model that could augment their workflow, which was a novel concept at the time. However, with early promising results, strong support from key business leaders — most importantly the CEO Kent Collier — and their belief in what was possible, I gained the endurance to solve problems and think bigger.
To stay closer to the ground of the methodologies and newest techniques of aspiring data scientists, I teach Data Science to Master’s students at the University of Virginia. This helps me stay aware of the latest trends and has made me more confident as a growing leader, sharpening my ability to communicate complex subjects in a simplified way.
We often learn the most from our mistakes. Can you share one mistake that turned out to be one of the most valuable lessons you’ve learned?
Looking back, one of the biggest mistakes I made was not keeping an eye out for talent in the early days of Reorg. I was so busy, neck-deep in solving immediate problems and securing business buy-in, that building a strong team fell off my radar. When it came time to accelerate, we struggled with finding the right talent. If I could go back, I’d focus on building a steady hiring pipeline and consistently networking, which is key to finding top-tier candidates for one’s team.
A.I. is a big leap for many businesses. When and what first sparked your interest in incorporating it into your operations?
When I joined Reorg as its first data scientist in 2015, the application of Data Science in the enterprise was in its early innings — and I saw the immense potential it held for transforming our own operations. I’m always looking for ways to innovate and push the boundaries of what’s possible with technology. Even without a traditional finance background, I knew we could achieve something incredible through our data and the implementation of technology. To make this data more actionable for our customers, who rely on Reorg to inform them of the financial decisions they make every day, there was only one solution — proprietary AI models. These algorithms serve to both create content at speed and to make it more accessible for over 35,000 professionals who use our platform.
AI can be a game-changer for individuals and their responsibilities. Can you share how you personally use AI and what are your go-to resources or tools?
I use a variety of GenAI chatbots, including ChatGPT, Claude, and Gemini, depending on the specific use case I have. When I’m on the go, I tend to rely on Llama through WhatsApp for quick checks. Each tool serves its own purpose, whether it’s driving deep into research or getting answers quickly and efficiently.
On the flip side, what challenges or setbacks have you encountered while implementing AI into your company?
For our customers, the right data at the right time is crucial. Without our data, they cannot make the market-moving decisions they do on a daily basis. We’re able to provide this critical data and analysis due to the nature of our relationship with our customers. However, this means that we deal with highly sensitive, private data from companies within very regulated sectors. One of our biggest challenges has been driving customer buy-in and comfort with the implementation and usage of AI within our technology and product.
To address this, we collaborated with our Security and Compliance teams to implement a number of responsible AI practices, including an AI trust center that includes the philosophies and principles that we abide by — such as data integrity and safety. We’ve also developed an AI governance framework, to foster and develop highly dependable and reliable tools. The framework includes various steps, such as testing our GenAI algorithms, to minimize common AI concerns. We recognize that mistakes and hallucinations may occur — but understanding where AI excels and where it doesn’t is crucial. To reflect our company-wide security posture, our AI environment will soon be SOC2 certified.
Let’s dig into this further. Can you share the top 5 AI tools or different ways you’re integrating AI into your business? What specific functions do they serve and what kind of results have you seen so far? If you can, please share a story or example for each.
1. CreditAI by Reorg™: We launched CreditAI by Reorg, our proprietary, interactive GenAI tool, last fall. Since its launch in October, CreditAI has transformed the way our customers access and distill credit information. The goal of this tool is to simplify the complexity of the data presented to our customers, providing direct access to our library of over 11,000 companies globally, dating back to 2018. To date, over 10,000 clients’ credit-related questions have been answered by CreditAI.
2. Generative AI models: Another great example of how we’re applying AI within Reorg is through our development of proprietary, fine-tuned generative AI models, specifically designed to enhance the efficiency of our editorial team. By implementing these AI models, we’ve achieved a dramatic decrease in the time it takes to publish content, while also expanding our overall coverage. For context, our Gen AI models have enabled us to develop over 35,000 summaries and more than 1,000 detailed stories with a 99% accuracy rate. This has allowed us to achieve a 50% faster publishing turnaround, which in turn allows our subscribers to receive timely and more accurate information at faster rates.
3. Support AI chatbot: Our self-service chatbot, designed internally, addresses basic user queries without requiring involvement from the Customer Success team. The chat feature can manage most routine inquiries quickly and effectively, while generating instant answers to standard questions regardless of time zone. Users can easily navigate to various support options directly within the Reorg platform, and our Customer Success and AI teams collaborate closely to thoroughly ensure clients get the best experience.
4. Extending CreditAI by Reorg to mobile: We’ve expanded CreditAI availability to Reorg’s mobile application, providing a new, on-the-go way to access and search through Reorg’s proprietary analysis via a conversational AI interface. The app is currently available on both iOS and Android
5. Summarizing transcripts and other long reports using expertly developed proprietary frameworks: By summarizing long reports using proprietary frameworks, we can help our internal users distill information quicker than ever before. We all know long form texts and documents can be hard to digest, especially when there are thousands of them every day. At the same time, simply providing a generic summary of them risks losing some vital information. That’s why we worked closely with our credit experts to carefully tailor what information is most useful to extract and share with our clients. This way, our clients benefit from both AI and our internal subject matter expertise.
There’s concern about A.I. taking over jobs. How do you balance A.I. tools with your human workforce and have you already replaced any positions using technology?
As I mentioned earlier, we view AI as a tool to augment and empower our workforce, rather than replace it. My goal as Head of AI Innovation at Reorg has always been to make our processes more efficient, allowing our team to focus on a broader reach as well as more substantial, higher-level tasks. We have not replaced any positions due to the implementation of the technology and have no plans to do so in the near future. Rather we will look to hire more data scientists as we continue to grow our team and continue to evolve with technology.
Reorg prioritizes ongoing education and training for our workforce by providing every employee with the tools needed to leverage AI effectively. Overall, I’m confident that AI will generate more jobs in the long run rather than eliminate them, and our experience at Reorg has already proven this.
Looking ahead, what’s on the horizon in the world of AI that people should know about? What do you see happening in the next 3–5 years? I would love to hear your best prediction.
Beyond ANI — Artificial Narrow Intelligence — that excels at specialized tasks, I foresee greater strides in AGI — Artificial General Intelligence — that can adapt more flexibly across domains. Models will become conversationally fluent, reason about context, and handle multidisciplinary problems. Imagine intelligent assistants that combine domain knowledge with business acumen to serve as versatile advisors.
Techniques in self-supervised learning, causal inference, graph neural networks and hybrid computing architectures will enable this cross-domain intelligence. Guarding against biases and misinformation will remain vital. Regulation around accountability and transparency will increase. OpenAI o1 (Strawberry) is already focusing a lot on inference reasoning, able to answer complex questions.
Economically, the boundary between jobs AI automates versus augments will remain fluid. I believe that the next half-decade will see AI transform from a set of specialized tools to cooperative partners in innovation and production. However, nurturing human judgment, ethics and oversight alongside will be critical.
If you had to pick just one AI tool that you feel is essential, one that you haven’t mentioned yet, which would it be and why?
Functional AI agents that can do specific tasks but communicate with each other to create a complete system is the next step. That kind of intricate orchestration, however, requires more clarity into some of the challenges — such as accurately understanding the context from each task and bringing it together for the user. There are some PoCs for this, but I have not seen anything in production at business scale as of yet.
For the uninitiated, what advice would you give someone looking to integrate AI into their business and doesn’t know where to start?
First, you need to establish the business value of implementing AI into your organization. Where will it make the most significant impact — for your employees and your customers? For many, the answer is automating repetitive and mundane tasks or making your data more structured and actionable. Once you’ve established the parameters of your use case, you need to establish the right team. When I joined Reorg, I was the first data scientist on our team. Together with our CEO, we identified our needs and built out the team that I currently lead. With the right focus and team in place, you can move forward with building algorithms and testing inputs to fine tune your outputs. Finally, it’s important to establish best practices and governance for your models, no matter how small.
Where can our readers follow you to learn more about leveraging A.I. in the business world?
You can follow me on my personal LinkedIn: Sreekanth Mallikarjun, Ph.D. | LinkedIn
And stay up-to-date on Reorg’s latest AI announcements on our website: https://reorg.com/
This was great. Thanks for taking time for us to learn more about you and your business. We wish you continued success!
About the Interviewer: Chad Silverstein, a seasoned entrepreneur with over two decades of experience as the Founder and CEO of multiple companies. He launched Choice Recovery, Inc., a healthcare collection agency, while going to The Ohio State University, His team earned national recognition, twice being ranked as the #1 business to work for in Central Ohio. In 2018, Chad launched [re]start, a career development platform connecting thousands of individuals in collections with meaningful employment opportunities, He sold Choice Recovery on his 25th anniversary and in 2023, sold the majority interest in [re]start so he can focus his transition to Built to Lead as an Executive Leadership Coach. Learn more at www.chadsilverstein.com
Sreekanth (Sree) Mallikarjun Of AI at Reorg: How We Leveraged AI To Take Our Company To The Next… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.