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Reliability Memory

Never relearn the same incident twice

NOFire builds a reliability memory from every incident, change, and fix so teams remember what broke, why, and how it was resolved.

2x
Faster Onboarding
80%
Less RCA Time

From incident postmortems to reusable reliability knowledge

How reliability memory compounds over time

Four capabilities that turn past incidents into future guardrails.

Have we seen this before?

Instantly surface similar incidents, root causes, and fixes by service, symptom, or failure mode instead of starting every investigation from zero.

How can NOFire AI help you?

Find root causes faster, prevent incidents before they happen, and learn from every investigation.

What have we learned from past checkout-service incidents?

Show me fixes for similar issues in payment-service

Generate alert rules based on orders-service failures

Recommend SLOs for checkout-service based on production data

You can @ services or infrastructure by name

Expose broken alerts before they wake you up

Automatically detect overlapping alerts, missing labels, and inconsistent thresholds and get concrete fixes, not dashboards.

can you review my alert rules?
!
Critical Issues Detected
Analyzed 11 alerting rules and found 3 problems: overlapping alerts, missing labels, inconsistent thresholds

Critical Issues

1. ServiceHighErrorRate / PaymentServiceCritical — Overlapping
• Both fire for payment-service errors at 5% and 15% thresholds
• Different time windows (30s vs 90s) create redundant alerts
• Currently: Both firing at 100% error rate
2. Missing Required Labels
• 3 alerts missing customer_facing
• All alerts missing runbook_url
• Prevents proper SLO tracking and incident response
3. FrontendHighErrorCount — Inconsistent Threshold
• Uses absolute count (50,000) instead of error rate percentage
Status: RED — Critical overlapping alerts for payment service and missing required labels prevent proper SLO tracking

Fix blind spots in your telemetry

Identify missing spans, broken context propagation, and semantic gaps in your OpenTelemetry data before they derail incident response.

check my OpenTelemetry instrumentation for orders-service
!
Critical instrumentation issues detected
Analyzed 20 traces across 2 services. Found missing resource attributes and incomplete trace coverage

Critical Issues

1. Missing service.version in CLI traces
• Present in HTTP traces but missing in CLI
• Inconsistent resource attribute configuration
Fix: Add to CLI instrumentation config
2. Limited CLI instrumentation coverage
• CLI: 3 spans per trace
• HTTP: 7+ spans per trace
• Missing database, external API, and middleware spans

Working Well

W3C TraceContext propagation across services
Proper error recording and status codes
All traces captured with proper relationships

Reduce observability cost without breaking reliability

Find high-cardinality metrics, wasteful retention, and unused signals while preserving RED, USE, and SLO coverage.

what about metrics cost improvements?
Analyzed 8,500 time series
53%storage reduction possible

Top Opportunities

1. traces_span_metrics_duration_milliseconds_bucket
70%
reduction
Current: 3,145 time series (highest cardinality)
Savings: 2,200 time series reduction
Action: Reduce histogram buckets from 26 to 8
Safe: Alerts use rate/sum calculations, not all buckets
2. container_blkio_device_usage_total
64%
reduction
Current: 628 time series (excessive granularity)
Savings: 400 time series reduction
Action: Aggregate to node-level only
Safe: Not in any alert rules, node-level sufficient
3. traces_service_graph retention
25%
storage
Action: Reduce retention from 90 to 30 days
Safe: Topology data rarely needed beyond 30 days
All RED/USE/SLO metrics preserved

What reliability memory unlocks

For developers

  • Resolve incidents faster using proven fixes
  • Stop relying on tribal knowledge
  • See how this service failed last time

For SRE & platform

  • One graph linking incidents, changes, and services
  • Ask: "Where have we seen this pattern before?"
  • Build preventive guardrails from real failures

For leadership

  • Fewer repeat incidents quarter over quarter
  • Reliability capability that compounds, not resets
  • Confidence that lessons from outages actually stick

Ready to stop repeating the same outages?