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Shift-Left Prevention

Stop failures before they deploy

Risk signals in IDE and PRs, before code ships

40%
Fewer Incidents
Get StartedSee Example

Shift reliability left with change intelligence

Four capabilities that map code changes to production risk before you commit or deploy

Assess change risk

Instant risk scores showing what breaks if your changes fail. Know the impact before you commit.

CursorBuild orders-service
Composer

is it safe to deploy my changes?

Let me analyze your code changes for deployment risk:
Rannofireai_get_dependency_depth
Rannofireai_assess_change_risk

Change Risk Assessment

Your changes affect orders-service with medium deployment risk

Files Changed:
• services/orders/routes.py
• services/orders/schemas.py

MEDIUM RISK (0.6/1.0)

Affected Service:
orders-service
•Blast radius: 15 services affected
•Critical path: checkout-service depends on this
•Recent activity: 3 deployments in last 7 days
Downstream Impact:
checkout-service- Direct dependency on orders
frontend-dashboard- 840 API calls/day to orders

Recommendation: Use canary deployment (20% → 50% → 100%). Monitor checkout-service error rates closely. Deploy during business hours for faster rollback if needed.

Quantify blast radius

Hop-by-hop dependency analysis showing exactly which services fail if yours does.

CursorBuild orders-service
Composer

if orders-service fails, what else breaks?

Let me analyze the blast radius for orders-service:
Rannofireai_analyze_blast_radius

Blast Radius Analysis

Analyzed 3-hop dependency chain from orders-service

MEDIUM IMPACT: 15 services affected

Direct Dependencies (1 hop):
checkout-service- Critical path (handles all checkouts)
frontend-dashboard- 840 API calls/day to orders
analytics-processor- Reads order events
Indirect Impact (2-3 hops):
payment-service- via checkout
email-service- via checkout
notification-service- via frontend
Plus 9 more services...

Risk Assessment: Medium (0.65/1.0). Failure would impact 1 critical service (checkout) with 8 downstream dependencies. Recommend staged rollout with close monitoring of checkout flows.

Detect risky patterns

Rollback history, change frequency, and deployment stability from recent activity.

CursorBuild orders-service
Composer

are there any risky patterns in orders-service deployment history?

Let me analyze deployment patterns for orders-service:
Rannofireai_detect_change_patterns

Deployment Pattern Analysis

Analyzed 30 days of deployment history for orders-service

Activity Summary:
• 12 deployments in last 30 days (avg 2.5/week)
• 2 rollbacks detected (16.7% rollback rate)
• Stability score: 0.72 (moderate)
Patterns Detected:
High change frequency - 12 deployments in 30 days suggests active development
Recent rollback 5 days ago due to performance degradation
Consistent deployment windows (85% during business hours)
Recent Change Timeline:
5 days ago: Rollback to v2.4.1 (API response time degradation)
12 days ago: Config change (timeout: 30s → 60s)
18 days ago: Feature deployment (add filtering to orders API)
Rollback Candidates:
No rollback candidates currently identified. Recent deployments are stable.

Recommendation: Use canary deployment due to recent rollback history. Monitor API response times closely (previous rollback reason). Deploy during business hours for faster rollback if needed.

Critical service awareness

Identify critical services in blast radius that require staged rollouts and extra caution.

CursorBuild orders-service
Composer

which critical services are affected by my changes?

Let me identify critical services in your blast radius:
Rannofireai_get_critical_services
Rannofireai_get_dependency_depth

Critical Services Analysis

Identified services requiring mandatory staging and canary deployment

1 critical service detected in blast radius

checkout-service(Criticality: 0.92/1.0)
•Revenue-critical: Handles all checkout flows and payment processing
•High blast radius: 8 downstream dependencies (payment, email, notifications...)
•Position: Entry point (upstream depth: 1)
•Risk: Single point of failure in checkout path
frontend-dashboard(Criticality: 0.58/1.0)
•High visibility user-facing dashboard
•840 daily API calls to orders-service
•3 downstream dependencies

Deployment Requirements:

checkout-service: MANDATORY staging + 10% canary rollout
frontend-dashboard: Monitor error rates during rollout

What preventing failures before deploy looks like.

For developers

  • Clear signals on risky changes in IDE and PRs
  • Fewer late-stage surprises from SRE reviews
  • Confidence to move fast without breaking prod

For SRE & platform

  • Fewer incidents triggered by known risky patterns
  • Evidence-backed conversations about change risk
  • Time back from manual change reviews

For leadership

  • Fewer customer-impacting incidents per release
  • Higher deploy frequency with lower risk
  • Protected roadmap and on-call capacity

Ready to make prevention the default?

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