DataGPT is a conversational AI data analytics software provider that provides analysis at the speed of business questions. Pulse 2.0 interviewed DataGPT CEO and co-founder Arina Curtis to learn more about the company.
Arina Curtis’ Background
Curtis has always used data analytics to develop practical business solutions. Curtis said:
“At PwC, I managed significant projects for major corporations across many sectors, such as energy, media & entertainment, banking, and telecommunications. My roles included implementing visualization dashboards, overseeing post-merger integrations, consolidating digital assets, developing long-term investment plans, and driving digital strategies. At US Mobile, I built and led a customer success framework, managing a team of over 150.”
“I earned my BSc in Economics at Lomonosov Moscow State University on a full scholarship, and an MSc from the London School of Economics. I have also written extensively, including research into dynamic pricing techniques, culminating in the book “Economic power of e-retailers via price discrimination in e-commerce” and several other notable publications.”
“Currently, as the CEO of DataGPT, I’m focused on strategic go-to-market approaches, establishing key partnerships, and enhancing brand visibility. As CEO, I focus on high-level strategic decision making and long-term planning for DataGPT. I work with the rest of the executive team to set goals, oversee our company’s direction, and ensure that we are successful. I’m often thinking about where DataGPT – and the bigger conversational AI Data analysis market – will be in five years, and working backward to plan how we’ll continue to stay at the forefront.”
Favorite Memory
What has been Curtis’ favorite memory working for the company so far? Curtis reflected:
“One particularly-cherished memory during my tenure at the company revolves around the testing of our Lightning Cache database. It was a collaborative effort with our exceptional CTO, whose ingenious problem-solving skills consistently astound us. As we put the database to the test, we were taken aback by its remarkable speed, outpacing even the most modern data warehouses. The defining moment came when we observed our head of data’s reaction, as he witnessed the database’s lightning-fast performance during rigorous data analysis. It quickly became clear that we were embarking on a journey that would leave an indelible mark in the history of data analytics.”
Core Products
What are the company’s core products and features? Curtis explained:
“DataGPT is a conversational AI data analytics software provider that delivers analysis at the speed of business questions. Our software empowers anyone, in any company, to chat directly with their data using everyday language, revealing expert answers to complex questions instantly. Our flagship product, The DataGPT AI Analyst, unites the creative, comprehension-rich side of a large language model (LLM) (the ‘right brain’), with the logic and reasoning of advanced analytics techniques (the “left brain”). This combination makes sophisticated analysis accessible to more people without compromising accuracy or impact.”
Challenges Faced
After asking Curtis about the challenges faced in building the company, she acknowledged:
“Popular solutions in our sector fall into two main categories:
- LLMs with a simple data interface (e.g LLM+Databricks); and
- BI Solutions integrating Generative AI
LLMs (Large Language Models) face significant limitations when it comes to analyzing rich datasets.
— Simplistic analysis is an issue. LLMs often translate user input into a single query, lacking the depth, breadth, and insightful analysis that complex datasets demand.
— Reliability and trustworthiness are essential in data analysis. Unfortunately, LLMs are prone to hallucinations, which can result in misleading or incorrect responses, posing a risk to the integrity of analyses.
— Computational and cost concerns are significant drawbacks. LLMs demand extensive querying and computational resources, leading to higher costs when dealing with complex data analysis tasks.
— Speed of analysis is another challenge. When confronted with intricate data queries, LLMs tend to lag in performance, which can hinder timely decision-making.
— Data connection hurdles and query limits are also in play. LLMs often have limitations regarding data context size and may not seamlessly integrate with common data platforms, further complicating the analysis process.
DataGPT though solves all of the problems faced by modern generative AI and delivers a new data experience, empowering any users to access comprehensive analysis by simply asking questions in everyday language.
The LLM is the right brain. It’s really good at contextual comprehension. But, the left brain, the DataAPI – our core analytics engine for logic and conclusions – is also necessary to provide this solution. Many platforms falter when it comes to combining the logical, ‘left-brained’ tasks of deep data analysis and the linguistic interpretation provided by the LLM.”
Evolution Of DataGPT’s Technology
How has the company’s technology evolved since launching? Curtis noted:
“The company’s technology has undergone a significant evolution since its initial launch. Over the past two years, our core technology has been meticulously developed, with each component specifically designed to address distinct challenges while maintaining interdependence with other elements. This synergy enhances the overall effectiveness of our technology.”
“Initially, our focus was on addressing the key pain points in data analysis, aiming to provide our users with robust, accurate, instant, and limitless data analysis and visualization capabilities. This effort resulted in the creation of the highly regarded data navigator, which quickly gained cult-like popularity among data analysts.”
“This year, we made a strategic transition to enhance user experience by combining the precision of our dedicated analytics engine with the user-friendliness of our chatbot – so we launched the first AI Data Analyst Agent. This innovation allows users to engage directly with their data through conversation, effectively expanding our product’s reach to business users. This evolution embodies our commitment to make the world data-driven, irrespective of the industry or role.
Significant Milestones
What have been some of the company’s most significant milestones? Curtis cited:
“DataGPT was founded in 2021, with the goal of pioneering the modern era of data analysis. Our mission is to empower every person, in every role, in any industry, to use data to make decisions, big and small. We started from scratch, developing proprietary technology that performs advanced data analysis swiftly and cost-effectively, essentially serving as a personalized data analyst.”
“Two short years later, in October 2023, we officially launched out of stealth and brought the Data GPT AI Analyst to product and UX, marketing, ad sales and customer service teams across a wide range of B2B and B2C industries, including fintech, e-commerce, gaming, media, entertainment, and travel.”
Customer Success Stories
After asking Curtis about customer success stories, she highlighted:
“Plex, a premier media streaming service, effortlessly delivers movies, TV shows, and music across a multitude of devices. With an immense amount of data to manage, keeping up with daily fluctuations can be an arduous undertaking. By choosing DataGPT, Plex empowered their team of analysts and business users to instantly identify meaningful changes in their data, like uncovering an issue affecting the conversion rate on a device partner. Through this discovery, Plex was able to bounce back from a 20% drop in new registrations on a certain device type and regain user trust.”
“Mobile games developer Mino Games first opened DataGPT and were alarmed to see revenue had declined by 30% compared to the prior month, resulting in a $100,000 loss. In one click, the Mino Games team discovered the loss was driven by a decline in in-app purchases from the user segment that was traditionally the highest spending. While the team had launched a new promotion aiming to increase in-app purchases, the inverse happened. Mino Games relaunched the promotion, adding in more relevant content for this user segment and changing the price point. Revenue increased once again, with DataGPT saving Mino Games $100,000 in 60 seconds.”
Funding
When asking Curtis about funding information, she revealed:
“DataGPT has raised $10 million in pre-seed and seed funding.”
Total Addressable Market
What total addressable market (TAM) size is the company pursuing? Curtis assessed:
“Big Data and Business Analytics Market Size is $271.83 billion, according to Fortune Business Insights. Because some companies still need first to complete data consolidation and cleaning before using DataGPT, we conservatively assume that we will initially target 10% of the market, which brings us to a TAM of $27.2 billion. Because DataGPT is its own product category and has a first-mover advantage in this sector integrating technology in a way that is several years ahead of alternative BI data analytics products, we are confident that we can easily penetrate this TAM.”
Differentiation From The Competition
What differentiates DataGPT from its competitors? Curtis affirmed:
“DataGPT bridges the gap between current business intelligence and consumer-facing generative AI tools that lack iterative querying (BI) and integration capabilities (AI). While there are several offerings today that offer certain of these features, there is no other platform like the DataGPT AI Analyst, which puts skilled analysis capabilities into the hands of the everyday user and the experienced data analyst alike.”
Future Company Goals
What are some of the company’s future company goals? Curtis concluded:
“The company has several exciting future goals on both the product and business fronts. In terms of product development, we are dedicated to expanding our data analysis capabilities in the near future. This expansion includes enabling cohort analysis, forecasting, predictive analytics, and other advanced forms of analysis. We also will work on adding some new features for our enterprise customers. Additionally, we’re exploring the implementation of an AI Assisted Schema Setup, which simplifies onboarding.”
“From a business perspective, we’ve experienced tremendous growth, with demand for our product skyrocketing. We are currently overbooked a month in advance, indicating a high level of interest from potential customers. To meet this demand, we are in the process of expanding our sales and onboarding teams. Furthermore, we are actively working on securing Series A funding to support our continued expansion and development. These goals reflect our commitment to delivering innovative solutions and sustaining our growth in the market.”
