Interviews: John Myers, Gretel.ai's Co-Founder and CTO, Answers Five Key Questions
In the heart of San Diego, a new startup named Gretel.ai is making waves in the tech industry by focusing on data innovation and privacy engineering. The company's mission is to create technologies that enable the safe and privacy-preserving use of sensitive data, revolutionizing how companies protect personally identifiable information (PII) while enabling data analytics and AI development.
Gretel.ai's primary tools include synthetic data generation and privacy-enhancing technologies. These technologies help mask PII in data, enhancing privacy without sacrificing data utility. For instance, they employ fine-tuned models to detect and mask PII, ensuring privacy risks are mitigated while maintaining data's value.
One of Gretel.ai's key innovations is synthetic data generation. This technique replaces real data with artificial but statistically similar datasets to minimise privacy risks, supporting data-driven projects while staying compliant with privacy regulations. The company also works on semisupervised defences against data leaks in AI systems, integrating human-in-the-loop mechanisms to improve detection and protection.
The Gretel team comprises veterans and former federal employees, whose past experiences have shaped the company's mission to address privacy challenges in developer workflows. The team's commitment to building a product that makes data sharing and access easier is further reinforced by the diverse group of professionals they have hired since launching.
Gretel.ai's synthetic data can be used to generate new records containing attributes that may be underrepresented in the original data, reducing biases in the analysis of that data. This synthetic data, often referred to as a "clone" of the original data, shares the same statistical properties and valuable insights as the original while ensuring privacy risks have been removed.
The startup offers a "developer" plan that provides access to its full suite of privacy engineering tools, which is free to get started. Gretel aims to enable teams and organizations to innovate with data by removing bottlenecks such as data silos and long approval processes for access to production data.
Gretel's core libraries are open source and will always be transparent to the developer community. The company also provides automation of systems in the cloud to remove the need for in-depth understanding of machine learning, and offers a "report card" outlining the usability and privacy of the synthetic data generated.
Developers can interact with Gretel's APIs using a cloud-native console, command line interface (CLI), or software development kit (SDK). Gretel aims to combine various privacy engineering tools under a common set of APIs for easy accessibility and scalability.
Synthetic data, alongside other privacy engineering tools like natural language processing and traditional data transform techniques, is considered the future in the creation and consumption of data. Tools like GitHub, GitLab, Airflow, Prefect, Airbyte, and Dbt can be used to operationalize privacy engineering with Continuous Integration / Deployment (CI/CD) workflows and Extract, Transform, and Load (ETL) automation.
In summary, Gretel.ai is a San Diego-based startup focused on helping developers use data efficiently while implementing privacy safeguards. Their innovative approach to data privacy engineering, particularly through synthetic data generation, is set to revolutionise the tech industry, making complex privacy engineering tasks more accessible and transparent.
- Gretel.ai's primary tools include synthetic data generation and privacy-enhancing technologies, designed to mask personally identifiable information (PII) in data.
- The company's mission is to create technologies that enable the safe and privacy-preserving use of sensitive data, revolutionizing data analytics and AI development while maintaining compliance with privacy regulations.
- The startup offers a "developer" plan that provides access to its full suite of privacy engineering tools, aiming to remove bottlenecks like data silos and long approval processes.
- Gretel's core libraries are open source and always transparent to the developer community, with automation of systems in the cloud to remove the need for in-depth understanding of machine learning.
- Synthetic data, alongside other privacy engineering tools like natural language processing and traditional data transform techniques, is anticipated to be the future in the creation and consumption of data.
- Gretel.ai is positioned to revolutionize the tech industry by making complex privacy engineering tasks more accessible and transparent through its innovative approach to data privacy engineering.
