Data management and debugging are the main challenges of any industry. The overburden of building in-house solutions for this and, at the same time, securing confidential data is troublesome and confounding.
Tonic.ai can let these troubles disappear! The experiences of the engineers with building fake data from scratch that created additional workload were the fuel to develop a platform to streamline the design of safe, realistic and practical data for developers. They generate data that looks, acts, and feels like your production data and safely shares it across teams, businesses, and international borders. Have a more insightful view on this through our interview with Tonic.ai.
What is the startup / product / venture about? Give us a brief description of it.
Tonic mimics your production databases to create safe, high-quality, synthetic data for software QA, testing, and development. Thousands of developers use data generated with Tonic daily to accelerate their CI/CD pipelines in industries as wide-ranging as healthcare, financial services, logistics, education, and e-commerce. Working with customers like eBay, Flexport, the NHL, and The Motley Fool, Tonic has grown its business by over 500% in the past 12 months. Founded in 2018, with offices in San Francisco, Atlanta, and New York, the company is pioneering enterprise tools for database sub setting, de-identification, and synthesis to empower developers while protecting consumer privacy.
How many co-founders are there? Please introduce them and their backgrounds.
Ian Coe, CEO
Andrew Colombi, COO
Karl Hanson, CTO
Adam Kamor, Head of Engineering
How did you come up with the idea? What motivated you to do this?
The four founders had frequently run up against the roadblock of not having access to realistic test data during their careers. A memorable instance involved Ian Coe, Andrew Colombi, and Karl Hanson when they worked together at Palantir in business development and engineering. They were sitting on-site late at night in the empty offices of a financial services customer trying to debug some failing code. They had a large, brilliant development team in Palo Alto eager to help, but no way to send the developers the data that was causing the problems. It was confidential client data containing a myriad of personally identifiable information (SSNs, phone numbers, salaries, etc). Ultimately, they had to build fake data from scratch, a process that took months, rather than the days they expected. These experiences inspired them to found Tonic.ai and build a platform to streamline the creation of safe, realistic, useful data for developers.
Who is your target market? Why do you think your product will appeal to them?
Our mission is to always be one step ahead of the complexity that software teams continue to face, as their products rely on larger and larger datasets. We see a future in which machine learning and AI will be used across the development process. That process’s interdependency on data operations, data engineering, and data science teams is only going to grow. The technology we built to revolutionize CI/CD for developers has equally valuable applications in generating quality sales demo environments, training machine learning models, and performing data analytics. Having proven the value of data mimicking in software development, we’re building out our tools to do the same for data science, DataOps, and sales engineering use cases. Our goal is to equip all teams on an organization’s data pipeline with the high-fidelity data they need to ensure compliance, improve the quality of testing, and accelerate development cycles. This holds across the full range of industries mentioned earlier.
Who are your competitors? How are you different from them?
Our biggest competitor right now is building in-house. The natural instinct for developers when they hit a bottleneck or a roadblock is to build their own solution. When it comes to creating fake data, people underestimate the complexity and difficulty of the work involved. They don’t imagine the time that it will take, the maintenance required to keep it running, or the headaches encountered when the solution they’ve built needs to scale to another use case or a larger dataset. Almost all of our customers come to us after having considered or attempted to build a solution in-house. Tonic.ai was founded by engineers who experienced that challenge and added workload themselves. Now Tonic.ai is providing organizations with a tool that takes that burden off of their developers so they can get back to building their products.
What are the future plans with the product/startup? Any new features you are planning on?
We are first and foremost focused on customer time to value, and we’re investing in both sides of that equation: getting smarter about how easy we make it to protect your data, and adding more and more value to the output. Our customers operate in complex enterprise environments, so we are also investing in integrations that enable Tonic to fit seamlessly into their workflows. And we’re expanding beyond development and test use cases to meet data teams where they are—whether that be for sales demos, data science and machine learning teams, or even lines of business.