Nikhil Smotra Simplifies Customer 360 at Dataworkz with Real-Time AI
Nikhil Smotra, Co-Founder and CTO at Dataworkz
Co-Founder and CTO at Dataworkz
Nikhil Smotra is the Co-Founder and CTO of Dataworkz. With a passion for optimizing business processes, understanding customer behaviors, and transforming traditional applications to Big Data architectures, Nikhil excels in building data engineering and analytics practices from the ground up and leading global teams.
Nikhil’s journey in Big Data started long before Dataworkz, as he served in various key roles across renowned organizations. He was a Senior Vice President and Head of Data Engineering at iQor, where he successfully led his team in implementing groundbreaking data engineering solutions that streamlined operations and enhanced decision-making processes. Earlier in his career, Nikhil was a Senior Consultant at Lockheed Martin and General Dynamics.
Nikhil also serves as an Advisory Board Member for Rutgers BigData, where they actively contribute to shaping the future of data science and analytics education.
Transcript
Being named a Digital Champion is a great honor. It inspires me to continue exploring new opportunities, pushing myself harder, exploring emerging technologies, and driving innovation.
We at Dataworkz are on a mission to simplify the process of creating customer 360 to enable organizations to tailor their products, services, sales, and marketing efforts to meet customers' demands and expectations.
The biggest problem we've seen with data is if you're trying to build a customer 360, You need to combine data from multiple systems the data is in different formats, and the schemas keep changing. There is no end-to-end solution which lets you build a customer 360. In addition to using multiple tools, there is no collaboration between different tools which are being used.
We are trying to reimagine the process of reading a single view for your customers using large language models. How we differ is that we've taken the tough approach of describing a data preparation problem in plain English, use or leverage a large language model to understand the semantics, convert that English into code which can run on Spark and execute it on the cloud.
We abstract the complexity of setting up the AI stack and let business users deal with the problem at hand. For Dataworkz, having a vector database is important to harness large language models for either question answering systems insights and intelligent summarization. Astra Vector DB is a really great addition for us. The reason is for leveraging last language models, we wanted to go ahead and use a vector database. In addition to that, we also wanted a very fast operational lookups store to store the chunk information. Having both those pieces available in a single product is great news for Dataworkz.
Being named a Digital Champion is a great honor. It inspires me to continue exploring new opportunities, pushing myself harder, exploring emerging technologies, and driving innovation.
We at Dataworkz are on a mission to simplify the process of creating customer 360 to enable organizations to tailor their products, services, sales, and marketing efforts to meet customers' demands and expectations.
The biggest problem we've seen with data is if you're trying to build a customer 360, You need to combine data from multiple systems the data is in different formats, and the schemas keep changing. There is no end-to-end solution which lets you build a customer 360. In addition to using multiple tools, there is no collaboration between different tools which are being used.
We are trying to reimagine the process of reading a single view for your customers using large language models. How we differ is that we've taken the tough approach of describing a data preparation problem in plain English, use or leverage a large language model to understand the semantics, convert that English into code which can run on Spark and execute it on the cloud.
We abstract the complexity of setting up the AI stack and let business users deal with the problem at hand. For Dataworkz, having a vector database is important to harness large language models for either question answering systems insights and intelligent summarization. Astra Vector DB is a really great addition for us. The reason is for leveraging last language models, we wanted to go ahead and use a vector database. In addition to that, we also wanted a very fast operational lookups store to store the chunk information. Having both those pieces available in a single product is great news for Dataworkz.