Neosync
Neosync is an open-source anonymization and synthetic data platform.
Neosync is an open-source platform that allows you to create anonymized or synthetic data and sync it across all environments for testing and machine learning. Companies in highly regulated industries such as FinTech, HealthTech, InsureTech and those with sensitive data can use Neosync to create production-like data to use for debugging and building features in lower-level environments without the security and privacy risk of using production data.
Latest News
Active Founders
Evis Drenova, Founder
Evis is the CEO/Co-founder of Nucleus. Before Nucleus, he was one of the first Product hires at Skyflow, a data privacy and security company, and led Product and GTM for the Fintech market from Seed -> Series B. Prior to Skyflow, he was Head of Product at Truedata, a Managing Consultant at IBM and started his career in Enterprise Sales at Experian and Oracle. He was born in Albania, grew up in Boston and currently resides in San Francisco.
Nick Zelei, Founder
Nick is the CTO/Co-Founder of Neosync. Prior to Neosync, he was a Staff Software Engineer at Newfront, a new-age insurance brokerage where he built and managed the Platform Engineering team from Series B -> Series D. He grew up outside of Cleveland, Ohio and currently resides in San Francisco.
Company Launches
We’re an open source data anonymization and synthetic data platform that companies like Intel, Siemens, C2FO, Alasco and others use to anonymize their sensitive production data and sync it to lower-level environments.
Today, we’re launching a new product designed to detect and anonymize PII data in free-form text.
These are two main use cases:
- Detecting and redacting PII data before sending it to an LLM for inference. If you’re building agentic systems or working with LLMs and sensitive data, you shouldn’t be sending your sensitive data to those LLMs. You can now use our API to first detect and anonymize that data and then send it to your LLM provider.
- Detecting and redacting PII data before training. You generally want to avoid training a model on PII (especially if others will be using it). You can use our API to detect and redact free-form text in training data so that you’re not training it on PII.
For example:
The text:
{ text: "Dear Mr. John Chang, your physical therapy for your rotator cuff injury is approved for 12 sessions. Your first appointment with therapist Jake is on 8/1/2024 at 11 AM. Please bring a photo ID. We have your SSN on file as 246-80-1357. Is this correct?"}
Would be transformed to:
Anonymization result: '{"text":"Dear Mr. \u003cREDACTED\u003e, your physical therapy for your rotator cuff injury is approved for 12 sessions. Your first appointment with therapist \u003cREDACTED\u003e is on \u003cREDACTED\u003e at \u003cREDACTED\u003e. Please bring a photo ID. We have your SSN on file as \u003cREDACTED\u003e. Is this correct?"}'
You can also customize this with custom allow/deny lists and even custom recognizers.
We’re already working with companies in Healthtech and Fintech on this and would love to open it up to more companies. If you’re interested in trying it out, shoot me a note at evis@neosync.dev, and I can get you a sandbox and free credits to trial it.
