Solutions
Reputation Networks
Go beyond self-reported claims with verifiable, data-driven reputation that contributors can carry across platforms.
Verifiable credentials
Build reputation scores based on verifiable activity, rather than self-reported claims, such as code contributions, product engagement, and peer endorsements.
Multi-dimensional scoring
Evaluate reputation across multiple dimensions, tracked by your analytics. Train sophisticated models that separate quality from quantity.
Cross-platform portability
Reputation scores that work across products and platforms, enabling users to carry their track record anywhere.
Anti-gaming measures
Sophisticated detection for attempts to game reputation systems through coordinated activity or artificial inflation.
Case Study
Featured Case Study
Reputation-based participant selection for community governance programs, powered by OpenRank
30K+
repositories indexed for trust graph
420
top-ranked developers as trusted threshold
22.9%
social graph overlap between participant groups
2
funding programs powered by OpenRank
EigenTrust scores replace self-reported credentials
Instead of asking participants to self-certify expertise, OpenRank seeded a trust graph with core project repositories and propagated trust via merged PRs, stars, forks, and issues — using OSO's index of 2,000+ GitHub organizations and 30,000+ repositories. The resulting EigenTrust scores ranked developers by genuine proximity to the project, not self-described affiliation.
Trust-weighted impact metrics for program applications
OSO and OpenRank jointly produced 15 per-repository metrics for every applicant — including trusted contributor counts (limited to the top 420 OpenRank-ranked developers) and trust-weighted star and fork scores representing each contributor's proportional reputation share. These gave reviewers verifiable, manipulation-resistant signal on project quality beyond raw GitHub statistics.
Manipulation resistance is structural, not just filtered
The bidirectional trust graph makes gaming difficult by design: user-to-repo signals (pull requests, issues) and repo-to-user signals (merged PRs, direct commits) must reinforce each other to produce high scores. A time decay function prevents outdated repositories from inflating rankings, and merged PR data is prioritized over opened PRs to reward genuine contribution over noise.
Read more: OpenRank · Voter Selection Algorithm · Impact Metrics