Full Context Embedded SRE
Prevent change-driven incidents, find root cause with high accuracy, stop repeating mistakes.
Used in production by teams shipping every day
Engineering teams use NOFire to prevent failures and resolve incidents faster.
“We spent hours jumping between dashboards, piecing together what happened. NOFire AI shows the entire failure chain instantly—root cause to every affected service. On-call engineers now make decisions confidently without escalating to senior SREs.”
How NOFire works
One system that connects changes, services, behavior,
and outcomes across time.
Ingests from your existing stack. No rip & replace.
Turns existing signals into decision-ready answers
No rip & replace.
From change to confidence
Prevention, resolution, and learning connected in one system.
See what this change will break.
Maps blast radius, historical incidents, and runtime dependencies to predict production impact.
Add Redis caching layer to cart operations
Risk Score: 8/10 (HIGH)
❌ RECOMMENDATION: WAIT
Do not merge or deploy at this time. cart-service is in critical path during peak shopping hours.
Reasons to Wait
- Peak traffic window (2,400 req/min), cart abandonment risk is critical
- checkout-service latency spike (p95: 2.3s, +180% from baseline)
- Redis cluster failover detected 45 minutes ago (still stabilizing)
- 3 services depend on cart-service: checkout, recommendation, frontend
⏰ Deployment Window Assessment
- Now (3:30 PM EST): Not recommended (peak shopping hours + Redis instability)
- Next safe window: Tonight after 10 PM EST (low traffic + Redis stable)
Hypotheses (3 tested)
Memory leak in cart session cleanup causing gradual pod memory exhaustion
Supporting Evidence
- Heap usage grew linearly from
400MBto1.2GBover 6 hours before OOMKill * - Session cleanup job last ran 8 hours ago (expected: every 30 minutes)
- Redis connection pool showed
400+idle connections (normal:<50) * PR #2847merged 6h before incident: "Refactor session cleanup to use async workers", disabled CronJob- Deployment
cart-service v2.14.3at08:23 UTC- OOMKills started at14:48 UTC(6h 25m later)
Increased traffic from bot activity overwhelming pods
Supporting Evidence
- Request rate increased 45% (
600 → 870 req/min) at14:23 UTC*
Contradicting Evidence
- User-agent analysis shows normal distribution of clients
- CPU usage remained stable despite traffic increase *
Know what broke with high accuracy.
Tests multiple hypotheses in parallel. Validates each with real evidence from your infra, code, telemetry, and change history.
Reliability Memory
Learns from past knowledge, incidents, and every interaction.
Similar incidents
- Dec 12 • cart-service OOMKill
- Jan 8 • cart-service memory exhaustion
- Jan 28 • Similar Redis connection pool issue
Common factor
- Session cleanup changes + cache client reconnect
Known fix
- Enforce cleanup cron + cap idle connections
- Add heap monitoring
Running causal analysis and gathering evidence from production sources in parallel...
Reliability stops being reactive
Prevent failures
Before changes reach production
Surface downstream impact and flag risky changes before they reach production.
Resolve incidents
When things break
Connect symptoms to the exact changes that caused them. Root cause in minutes, not hours.
Learn continuously
After every incident
Every incident strengthens future deploy decisions. Systems learn instead of repeating failures.
Built for production.
Trusted by security teams.
Read-only access
NOFire observes system behavior without modifying infrastructure or data.
No write operations
NOFire never modifies your infrastructure or applications.
Data isolation guarantee
Your organization's data remains completely isolated from other customers
No model training on your data
Your data is never used to train models.
VPC PrivateLink support
Secure private connectivity without exposing data to the public internet
Data retention
Set custom retention policies and automated data purging schedules