Solutions
Fraud Detection
Protect your systems from manipulation with data-driven fraud detection.
Sybil detection
Identify fake accounts, duplicate submissions, and coordinated manipulation.
Activity anomalies
Flag suspicious spikes in activity or interactions. Train models that identify outliers and patterns indicative of fraud specific to your systems.
Network analysis
Map relationships between entities in your semantic model to surface hidden coordination.
Scalable enforcement
Monitor complex criteria at scale, automatically filtering out fraudulent activities.
Case Study
Featured Case Study
Filtering thousands of grant applicants to surface only credible, high-impact builders
1K+
applicants scored across funding rounds
4 yrs
open source contribution history analyzed
100%
approved projects verified for on-chain activity
Multi-signal scoring replaced manual review
OSO built a scoring pipeline that evaluated every Gitcoin Grants applicant across open source contribution history, on-chain activity, and developer retention, spanning four years of data. Rather than relying on self-reported credentials or human reviewers at scale, the model produced a defensible, data-driven score for each project that donors and badgeholders could audit and trust.
Manipulation and low-quality applicants filtered before the vote
Projects with inflated metrics — coordinated starring, bot-driven transaction volume, or activity concentrated entirely in the grant application window were flagged by anomaly detection before reaching the voting stage. The result was a shortlist where every visible project had demonstrated sustained, genuine contribution, dramatically reducing the surface area for Sybil attacks and gaming.
Developer retention as a signal for long-term impact
OSO weighted developer retention and reputation heavily in the scoring model: projects that retained high-quality active contributors across multiple quarters scored significantly higher than those with one-time spikes. This surfaced builders whose teams were genuinely committed to their work, not just optimizing for grant eligibility windows — giving Gitcoin a reliable proxy for durable impact rather than short-term activity.
Repeatable infrastructure across funding rounds
The scoring and filtering pipeline was designed to run across successive Gitcoin Grants rounds without manual re-configuration. As new rounds launched, OSO's data models ingested updated GitHub and on-chain activity, re-scored applicants with fresh signals, and produced updated shortlists, giving Gitcoin a consistent, auditable standard for project quality that compounded in reliability over time.
Read more: Gitcoin on OSO