Use Case
Release Readiness
Aggregate signals via MCP + API from errors, CVEs, and CI pipelines into a single readiness score. Your AI model scores risk. Ship with confidence, not hope.
The Problem
You are about to approve v2.4.0 for production. CI is green. But is it really safe? You Slack three team leads asking about open issues. One says “I think we are good.” Another does not respond until after lunch. The release slips to 3pm — and you still are not sure it was ready.
Without a unified view, “ready to ship” is a gut feeling. Teams either deploy blind and deal with the fallout, or delay releases while someone manually checks five different dashboards.
The Solution
SignalManager AI ingests signals via MCP server or REST API from every connected source — error rates, open CVEs, CI status, and recent deploys. Your AI model weighs each signal and computes a single readiness score that tells you whether it is safe to ship.
- Readiness score — a single 0-100 number combining all signal sources
- Signal aggregation — Sentry errors, NVD CVEs, CI results, and Datadog metrics in one view
- Deploy gates — block or warn on deploys when the score drops below your threshold
How It Works
Connect via MCP or API
Link Sentry, GitHub, your CI provider, and optionally Datadog via MCP server or REST API. SignalManager AI starts collecting signals immediately.
Your AI Scores Risk
Your AI model weighs each signal by severity and recency. The readiness score updates in real time as conditions change.
Ship or Hold
Check the score before deploying. Optionally, add a CI gate that blocks merges when the score is below your configured threshold.
Results
Fewer post-deploy incidents
To answer “is it safe to ship?”
Signal sources in a single view
Go/no-go decision without chasing anyone
Explore More
Ship with data, not gut feelings
Connect your error trackers, CVE feeds, and CI tools. Get a release readiness score in minutes, prioritized by revenue impact.