AI engine
Inside the agent swarm behind every CodeQuity memo
Watch specialized agents cross-check fundamentals, technical velocity, sentiment, on-chain adoption, and macro context before a signal reaches investors.
AI agent swarmSwarm visualization of 8 CodeQuity AI agents working in parallel.
Agent activity
Founder qualityactive
Revenue modelanalyzing
Repo velocityactive
Release cadencealerting
Community growthactive
Narrative fitanalyzing
Wallet retentionactive
Cycle contextidle
Specialized agents
35+
Signal loops
Parallel
Memo stance
Explainable
Relationship layer
One graph explains why a startup signal matters.
CodeQuity links source data to explainable score movement so investors can compare quality, not just activity volume.
Signal network graphNetwork graph with 6 nodes and 7 weighted relationships.
Network relationships
GitHubdeveloper
On-chainprotocol
Marketsmarket
Fundingcapital
Socialsentiment
Traction Scoreoutput
Metric orbsFloating metric orbs showing 4 weighted traction indicators.
Metric summary
Signal Confidence91%
Coverage Depth84%
Noise Rejection73%
Review Latency6m
Signal Confidence91%
Metric confidence
Every metric keeps its context.
Internal trends, confidence, and latency are shown beside the score so the public page remains inspectable instead of decorative.