Rakshak AI reads every GitHub change and scores it against your own production incident history and known CVEs — mapping blast radius, weighing the exact files that broke before, then gating the release to ship, review, or block.
Live decisions
refactor: payment webhook retry + coupon stacking + proration + orders_meta migration
#1471 · Muneerali199
Subscriptions & BillingCheckout FlowData Migrations
chore: pin axios to 1.2.3
#1482 · Muneerali199
Subscriptions & BillingCheckout FlowData Migrations
refactor: payment webhook retry + coupon stacking + proration + orders_meta migration
#1471 · Muneerali199
Subscriptions & BillingCheckout FlowData Migrations
fix: resolve 4 bugs in Draftdeckai
#1141 · saurabhhhcodes
apicomponents
fix: resolve 4 bugs in Draftdeckai
#1140 · saurabhhhcodes
apicomponentsextension
fix: resolve 4 bugs in Draftdeckai
#1139 · saurabhhhcodes
apicomponents
How it works
Every pull request, commit, and repo event arrives from GitHub with its full diff, labels, and linked issue references.
Rakshak AI pulls related Jira issues, architecture notes, release reports, and the incident postmortems that matter for the touched files.
An AI reasoning layer explains the change in plain language, names the affected systems, and forms concrete failure hypotheses from the raw evidence.
A deterministic safety engine maps files to modules, walks the downstream dependency graph, and scores release risk from scope, fragility, coverage, history, and known CVEs.
The decision becomes an enforced side effect: a Jira blocker, a Notion report, a Netlify preview, or an approved ship — and the merge gate follows it.
Not a code review clone
What can break?
Blast radius mapped from the real dependency graph — not a file list.
Which parts of the product are affected?
Module-level impact with severity, coverage, and downstream services.
Has this area caused incidents before?
Postmortems and incident history are loaded from Notion and weighed into the score.
Is this safe to ship now?
A single release risk score with a hard decision on top.
What should happen next?
The agent files the blocker, writes the report, or triggers the preview itself.
Decision system
Contained, tested, low risk — approved for production.
Human sign-off required; routed through preview and a Jira follow-up.
Do not merge. A blocker is filed, the release report updated, and the gate stays red.
Built on the surfaces your team already runs
GitHub
PRs, commits, diff context
AI Agent
LLM reasoning over every change
Jira
Blockers & follow-up issues
Notion
Architecture, reports, incidents
Netlify
Preview & deployment actions
Swytchcode
Orchestration layer
Try it on the demo workspace — analyze a PR and watch the agent map the blast radius and take action.