Enables AI clients to analyze software test results and defect records, producing explainable GO, CONDITIONAL_GO, or NO_GO release-readiness recommendations with deterministic risk scoring, failed-test retrieval, defect hotspot ranking, and regression test planning.
MCP-native release confidence control plane that turns browser execution into auditable go/no-go decisions by combining business-critical journey context, evidence-heavy QA runs, and risk governance.
Enables acceptance gates for AI coding-agent runs by recording evidence, running deterministic validation, applying a quality gate, and rendering auditable outcomes.
An evidence-first integration decision service that assesses GitHub repositories and returns transparent scores, adoption recommendations, and verification gates.
Enables building structured release evidence by comparing two Git refs and correlating commits, merged pull requests, linked issues, labels, changed files, and contributors.
