I built GovernKit because I have seen how data leaves companies.

Hundreds of companies used iScraper.

Building iScraper forced me to understand data engineering and API design at a very practical level. At that scale, you learn where data lives, how it moves, and where the boundaries are. You also learn how seriously large companies take their terms and data access rules.

Then I learned how serious those boundaries can become.

I went through a major legal dispute with LinkedIn about data scraping and Terms of Service enforcement. I saw how quickly a technical decision can become a legal and financial problem when data boundaries are unclear.

Terms of service are not background noise. They shape what your company can safely do with a tool, and the cost of getting them wrong can be high.

Today, the risk often starts inside the company.

After iScraper, I noticed a blind spot in corporate IT. Employees were adopting AI tools faster than companies could review them. Proprietary code, client emails, and PII can end up in tools no one has approved yet. Many of those tools have terms that affect training, retention, sharing, or deletion. Most employees will never read those terms before pasting data in.

So I turned that experience into a defensive product.

I built GovernKit because I understand how these systems work under the hood. It reads AI vendors' privacy policies and terms of service, cites their own words back to your reviewers, and keeps checking for changes. Your team can adopt AI with speed, but with a real record of what was reviewed and why.