Case Study: Sequoia Capital achieves bias-audited startup sourcing with AppRocket

Sequoia Capital builds a bias-audited 23-signal startup scoring model with AppRocket

The customer, Sequoia Capital, faced the challenge of building a data pipeline to identify promising startup investments earlier. They needed a scoring model that would avoid reinforcing existing venture capital biases. They engaged AppRocket's CEO as an INSITE Fellow to address this.

AppRocket analyzed 73 candidate signals and manually reviewed hundreds of companies to build a ground-truthed model. Through bias auditing and refinement, they created a 23-signal scoring model. AppRocket's solution was tested on 50,000 companies and then successfully integrated into Sequoia Capital's sourcing algorithms, making them more precise and scalable.

Sequoia Capital