REX
Reputation × Expertise — a peer-review system for funding decisions in grant programs, DAOs and foundations.
REX is TrustLevel's peer-review system for funding decisions. It helps grant programs, DAOs and foundations evaluate proposals in a way that is structured, transparent and hard to game — by weighting each review according to the reviewer's Reputation × Expertise (hence the name).
REX grew directly out of our work redesigning the proposal-review process for Project Catalyst (Cardano's community funding program). It is a custom product, not a self-serve app: it is deployed and operated for a specific program. For Catalyst it runs in production; for other programs it is available on request.
Why it exists
Open, community-driven funding has a recurring problem: reviews vary wildly in quality, expertise rarely reaches the proposals that need it, and there's little accountability for a careless or biased assessment. REX addresses this by making review quality itself measurable — and by letting a reviewer's track record shape how much their voice counts.
How it works
Two-stage review. Every proposal goes through two layers:
Proposal reviews — reviewers assess a proposal against a structured framework.
Peer reviews — other reviewers evaluate the reviews themselves, which is how review quality is scored and reputation is earned.
Six evaluation criteria. Each proposal is rated across Relevance, Innovation, Impact, Feasibility, Team and Budget, with descriptive anchors for each rating. A quick temperature check up front decides whether a full review is warranted.
Expertise-based matching. Proposals are routed to reviewers by a tiered match — technical/community/product expertise first, shared interests second, random assignment only as a fallback. Conflicts of interest are excluded automatically.
Reputation. Peer ratings feed a reviewer's reputation over time, which in turn influences matching and standing. Good, specific, insightful reviewing is rewarded; low-effort reviewing is surfaced and down-weighted.
AI assistance. Language-model summaries condense long proposals so reviewers can focus their judgment where it matters.
Who it's for
Grant programs, DAOs, foundations and any funding body that runs an open or semi-open review round and wants more reliable, accountable outcomes than unstructured community voting provides.
Learn more
Source code and full technical documentation: github.com/TrustLevel/Grant_Review_Tool
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