Services · Observability & SRE

Find the failure
before your users do.

We instrument your systems with OpenTelemetry, define SLOs that mean something, and wire alerts to automated remediation — cutting MTTD from hours to minutes.

47min→4minMean time to detect
62%Incidents auto-remediated
85+Services instrumented, one engagement

Architecture

Full-stack observability pipeline

Three pillars — traces, metrics, logs — unified in one OTel collector, stored in purpose-built backends, visualized in Grafana, and wired to automated remediation for the most common incidents.

ANAGHA OBSERVABILITY & SRE REFERENCE ARCHITECTURE INSTRUMENTATION COLLECTION THREE PILLARS PLATFORM Instrumented Svcs OTel SDK · Auto-instr. eBPF · APM Agents OTel Collector Receive · Process · Export Sampling · Enrichment Tail-based Sampling Metrics Prometheus · VictoriaMetrics Thanos · M3DB Traces Jaeger · Tempo Zipkin · Honeycomb Logs Loki · OpenSearch Elastic · Cribl Grafana / Dash Grafana · Kibana PagerDuty · OpsGenie SLO · Error Budget Alerting PagerDuty · Slack Alertmanager SRE Platform SLI · SLO · SLA Blameless · FireHydrant Chaos Engineering Chaos Mesh · Gremlin LitmusChaos Auto-Remediation Runbook Automation Keptn · Robusta

Our Approach

Observe everything. Alert on what matters.

01

Auto-Instrumentation First

OTel auto-instrumentation agents for Java, Python, Go, and Node — zero code changes for 80% of coverage. Manual spans only where business context needs it.

02

SLO Design Workshop

We run a structured workshop to define SLIs and SLOs with your engineering and product teams — based on user journeys, not server CPU. Error budgets become a shared language.

03

Alert Pruning

We audit existing alerting and ruthlessly remove noise — typically 60–70% of alerts in an average mature system fire without leading to action. We leave only actionable alerts.

04

Runbook Automation

The top 10 most common incidents get Ansible or Python runbooks triggered automatically. Your on-call engineer wakes up to a fixed system, not a pager.

What We Solved

Real engagements, measurable outcomes

FinTech · Full-Stack Observability

OTel instrumentation across 85 services for a payments platform

A FinTech company had per-team observability silos — Datadog in one squad, CloudWatch in another, ELK in a third. Cross-service traces were impossible. MTTD averaged 47 minutes.

OTel Collector deployed as a DaemonSet, auto-instrumentation agents for all Java/Python services, Grafana + Tempo + Loki stack, unified dashboards, and SLO error budget burn alerts per customer-facing API.

47→4 minMean time to detect
68%Reduction in alert noise
$420KAnnual observability tool consolidation
E-commerce · SRE Program

SLO program for 23 customer-facing APIs for a top-10 retailer

Engineers were reactive — learning of problems from customer support tickets. No SLOs, no error budgets. Product and engineering argued over what "reliable" meant.

OpenSLO spec definitions for 23 APIs, Sloth to generate Prometheus recording rules, Grafana SLO dashboard for engineering leadership, monthly error budget reviews with product.

23APIs with live SLOs
Fewer production incidents in Q2 vs Q1
Logistics · On-call Automation

62% of incidents auto-remediated for a 3PL operator

On-call rotation was burning out engineers — 3–5 alerts per night, most requiring the same 4-step remediation (pod restart, cache flush, circuit breaker reset, scale-up).

AlertManager with receiver-level routing, Ansible playbooks for the top 8 runbooks, Slack bot with "approve auto-fix" for medium-severity alerts, full audit trail in Jira.

62%Incidents auto-remediated
2.1hr/wkOn-call burden reduction per eng

Technologies We Deploy

The bench behind the build

OpenTelemetry Prometheus Grafana PagerDuty Grafana Tempo Grafana Loki Jaeger Thanos AlertManager OpenSLO Sloth Datadog New Relic Elastic APM AWS CloudWatch Ansible VictoriaMetrics

Ready to know before your users do?

We run a free 1-hour observability maturity assessment to identify the biggest gaps.