80% of all business data is structured data in the form of tables and databases but regulatory constraints like HIPAA, CCPA & GDPR make it difficult and costly for enterprise data-teams working regulated data. dbTwin is a enterprise platform that unlocks secure, data-driven innovation in sectors like healthcare, finance and tech by letting data-teams create private but realistic versions of their databases critical for training AI models, prototyping analytics workflows and functional testing of software/products.
80% of all business data is structured data in the form of tables and databases. Regulatory constraints like HIPAA, CCPA & GDPR make it difficult and costly to run AI, analytics, and software development workflows on structured data. With the synthetic data market projected to grow from $600M in 2025 to $4.9B by 2032 at a 35% CAGR, there is an urgent need for scalable, efficient, and privacy-compliant solutions.
dbTwin is a state-of-the-art synthetic data generation platform that is 20x faster and more cost-effective than deep-learning-based alternatives. Using a proprietary, GPU-free, and training-less approach, dbTwin produces high-quality, realistic synthetic data to augment existing datasets and fill gaps in diversity and accessibility. dbTwin helps enterprise data-teams turbocharge their AI workflows, accelerate software QA cycles by automating test data generation, simplify client onboarding with realistic demo data, and ensures privacy-safe collaboration by generating synthetic versions of sensitive datasets. dbTwin is primed to reduce compliance risks, improve access to critical data, and provide realistic datasets essential for prototyping and testing, enabling industries like healthcare, finance, and technology to unlock secure, data-driven innovation.
Our team combines deep technical expertise and proven operational success.
Aditya Nanda, Co-founder and CTO. Aditya has five years of specialized research experience in synthetic data at Vanderbilt University, where he developed NullSim - a proprietary synthetic data technology for timeseries data. With a PhD in Mechanical Engineering and degrees from IIT Kharagpur, Aditya’s groundbreaking innovations provide a 20x efficiency edge in synthetic data generation, uniquely positioning dbTwin to disrupt the market.
Ashish Patel, Co-founder and CEO, scaled Skylight, a smart home startup, from $25M to $110M as Head of Operations and Customer Experience. At GE, he led a team to industrialize and scale ceramics technology for the world’s largest engine. With a BS in Physics and an MBA from UT Austin, Ashish leverages his technical and business expertise to drive dbTwin’s operational excellence and market scalability.
Our go-to-market strategy follows a phased approach targeting consulting firms and AI companies. We aim to first validate our product with startups and smaller consulting firms, using their feedback to refine our solution before transitioning to enterprise customers. This approach builds credibility, develops case studies, and addresses a less demanding segment of the market.
We plan to launch a functional website by mid-January 2025 as part of a soft launch to attract early adopters. This includes a free trial tier supported by pricing tailored to team and enterprise users. Our web console and API will serve as primary delivery channels, with plans for future integration on cloud platforms like AWS, GCP, and Azure.
Partnerships will be key to our growth. We're in chats with consulting and analytics firms to demonstrate dbTwin’s capabilities in generating diverse AI model data, automating test data for software QA, optimizing analytics workflows, and creating demo data for onboarding and launches. These partnerships will help position dbTwin as a trusted solution, leveraging their networks to drive expansion into enterprise markets. Additionally, we are pursuing technical partnerships with cloud providers to enhance scalability and visibility.
We’ve secured funding for a 4-5-month runway and are actively raising a pre-seed round, targeting a close by March 2025. This funding will allow us to grow our team, further develop our technology and product offerings, onboard customers, and establish dbTwin as a key player in synthetic data generation.
We will generate revenue through a combination of a sales-driven model focused on building strong relationships and a marketing-driven approach that leverages developer communities.
Our sales strategy will target industries like analytics, AI, and consulting, where access to high-quality synthetic data is essential for testing, prototyping, and workflow optimization. By highlighting dbTwin’s unique advantages – 20x faster, GPU-less data generation for regulated and proprietary structured data – we aim to secure partnerships with organizations of all sizes. Our tiered pricing model ensures flexibility: the Free tier provides 5 credits/month for entry-level usage, the Team tier offers 180 credits/month for $180, and the Enterprise tier delivers 180+ credits/month with custom pricing. Each credit translates to either 12k rows of Core records or 6k rows of Flagship records, enabling tailored solutions for both lightweight and complex use cases.
In parallel, we will drive adoption through a marketing-driven model by engaging developer communities such as GitHub and Stack Overflow. These communities of data scientists and engineers will use dbTwin for testing, training, and prototyping, providing valuable feedback to improve our product while experiencing its benefits firsthand. This approach not only enhances the product but also fosters advocacy as users share their success stories, promoting dbTwin organically within their networks. By combining direct sales outreach with community-driven advocacy, dbTwin will establish a strong foothold in the synthetic data market, driving consistent adoption and recurring revenue from a diverse customer base.
At dbTwin, our ask to investors is a strategic partnership that involves both financial investment and access to resources that will enable us to scale our operations and realize our long-term vision to disrupt the structured synthetic data market. We are seeking $400K in funding to fuel growth in the following key areas:
Customer Acquisition & Marketing: Acquiring customers and early users is a priority for dbTwin. We will use funds to acquire new customers through partnerships, community engagement and targeted marketing campaigns.
Product Development: We will use a significant portion of the funding to steer dbTwin towards Product-market fit by incorporating user feedback and developing new features.
Talent Acquisition: Building a skilled, passionate team is essential to our success. We plan to hire top-tier professionals in engineering, marketing, and customer support to strengthen our capabilities and improve operational efficiency.
Scaling Operations: We will invest in infrastructure to support growth, including upgrading our technology stack, streamlining logistics, and ensuring we have the resources to handle increased demand while maintaining a high-quality customer experience.
Here are major milestones in dbTwin’s journey:
Feb 2023: Disclosed "NullSim" technology to Vanderbilt Tech-transfer office.
Sept 2023: Regional I-Corps program and initial customer discovery
Nov 2023: Finalized application to National Science Foundation I-Corps (& accepted)
May–June 2024: Attended 7-week NSF I-Corps program. Attended 5+ cloud and Big Data conferences and conducted 130 + customer interviews of analytics/AI companies that work with regulated data.
Dec 2024: Signed full Commercial license with Vanderbilt Tech office for NullSim
Dec 2024 : Obtained LOI from a data-engineering company Our intensive customer discovery efforts revealed analytics and AI companies as our beachhead market and reinforced the value of our efficient approach to synthetic data generation, guiding the development of our MVP.
We are on track to soft-launch a fully functional website by mid-January 2025 to attract early adopters. This includes a free trial tier supported by pricing tailored to team and enterprise users. And, we are in talks with dozens of companies, aiming to secure our first customers by Q1 2025. Meanwhile, we continue enhancing the speed, quality, and usability of dbTwin to meet the needs of analytics and AI companies. With a strong foundation and clear market focus, dbTwin is poised to make a meaningful impact in the synthetic data industry.
Regional I-Corps microgrant Sept 2023 - $2500 for initial customer discovery
National Science Foundation I-Corps grant Apr 2024 - $50K in non-dilutive funding to fund customer discovery
