DrDroid

AI that understands your entire infrastructure

Using DrDroid, every engineer on your team debugs like your best one.

Trusted by SRE, DevOps, and Infrastructure teams at

What makes DrDroid different

Your infrastructure, fully mapped — before the first investigation

DrDroid connects to your existing tools and builds a complete context graph — services, repositories, deployments, dependencies, clusters, logs, metrics, traces, and team ownership. Agents use this context to answer questions the way your best engineers would.

Your tools today Grafana Datadog Kubernetes ArgoCD GitHub PagerDuty Jenkins Slack CloudWatch Loki + 70 more Each tool is a silo. No tool knows about the others. DrDroid Connects to all tools Maps services, repos, deploys, dependencies, clusters, teams Builds context graph before first query Infrastructure Knowledge Graph order-svc order-repo payment-db prod-cluster Grafana logs DD metrics Team: Platform repo depends runs on logs metrics owned by Agent uses full graph for every question Other AI tools query individual tools. DrDroid understands your entire infrastructure.

What infrastructure intelligence unlocks

When AI understands your full stack and not just individual tools, it can do things like your best engineers can.

Investigations

Any engineer can run a senior-level RCA

Today, only your most experienced engineers know which logs to check, which service depends on what, and where to look when something breaks.

Because DrDroid already understands your full infrastructure — services, dependencies, deployments, and ownership — any engineer can ask a question and get an answer with the depth and context of your best SRE.

Watch investigation videos
ALERT: order-svc pods in CrashLoopBackOff (prod, us-east-1) Investigating... Agent investigation trail 1 Checked pod status and events 3/5 pods in CrashLoopBackOff — exit code 137 (OOMKilled), memory at limit 512Mi Kubernetes 2 Checked memory usage trend Memory growing linearly from 180Mi to 512Mi over ~8 min after startup — classic leak Grafana 3 Checked recent deployments order-svc v2.8.0 deployed 25 min ago via ArgoCD — previous v2.7.3 was stable ArgoCD 4 Compared release diff (v2.7.3 → v2.8.0) Found: added opentelemetry-sdk v1.28 + batch span processor with no memory bounds GitHub 5 Confirmed root cause OTel batch processor buffering unbounded spans — memory grows until OOMKilled v2.7.3 had no OTel SDK — no memory issues. Rollback is safe, no schema changes. Datadog Root cause: opentelemetry-sdk v1.28 added in v2.8.0 — unbounded batch processor Recommendation: Rollback to v2.7.3 (safe). Then re-deploy with maxQueueSize=2048 and maxExportBatchSize=512 configured on the span processor. 5 tools queried Completed in 2 min 14s Manual estimate: ~45 min No runbooks needed
Proactive Checks

Catch what no single alert can

Silent failures slip through because they span multiple signals — no single metric threshold can catch them.

Write a check in plain English and schedule it on a cron. The agent correlates across metrics, logs, and cluster state to catch degradation patterns that individual alerts would miss.

Watch how it works
Step 1 — Engineer creates a proactive check Check: "k8s cluster node health" "Check node CPU/memory pressure, pod eviction rates, disk I/O on etcd nodes, kubelet restart counts, and pending pods across all node pools. Flag if any node is silently degrading." Scheduled: every 30 minutes Too complex for a single alert Requires checking node metrics, kubelet, etcd & pods together Agent handles it instead Step 2 — Agent runs the check every 30 minutes 9:00 9:30 10:00 10:30 11:00 ! 11:30 Issue found Agent catches silent degradation across multiple signals node-pool-b silently degrading Disk I/O latency 3x on etcd nodes + kubelet restarts trending up 12 pods pending on node-4 + memory pressure at 87% (no alert set) No single metric would trigger an alert — pattern across 5 signals Team fixed it proactively Before pods started crashing or workloads got disrupted
Alert Intelligence

Your alerts, understood and processed

Too many alerts — most are noise, and real issues get buried. Existing tools deduplicate but don't understand what's actually happening.

Because the agent knows your architecture — which services are related, what was recently deployed, who owns what — it groups alerts by actual root cause, suppresses noise it has learned to ignore, and escalates by real impact.

Watch how it works
Incoming alerts (last hour) CPU high — checkout-svc CPU high — checkout-svc Disk 80% — logging-node-3 p99 latency — payment-api Memory warn — cache-01 5xx spike — checkout-svc CPU high — checkout-svc Cron missed — report-gen Disk 80% — logging-node-3 Connection timeout — payment-db ... +23 more 34 alerts DrDroid Agent Deduplicate 3x CPU checkout → 1 2x Disk logging → 1 Group by root cause checkout-svc cluster Classify by impact What your team sees P0 checkout-svc degraded CPU spike + 5xx + payment-db timeout Root cause: payment-db connection pool Impact: checkout flow down Page on-call P2 Disk filling on logging logging-node-3 at 80%, trending up Non-urgent — ticket created Suppressed (non-actionable) Cron missed — report-gen (known flaky) Memory warn — cache-01 (auto-scales) 34 alerts → 2 actionable 94% noise reduction Learns over time what's actionable
Knowledge Transfer

Stop losing context when engineers leave or rotate

Tribal knowledge walks out the door every time a senior engineer leaves. New hires take months to learn which dashboards matter, how services connect, and where to look when things break.

DrDroid captures your infrastructure context and investigation patterns in a persistent knowledge layer — so institutional knowledge lives in the system, not in people's heads. New hires are productive in weeks, not months.

First time — Senior SRE investigates manually ALERT: payment-svc p99 latency > 2s 1. Check redis-payments-03 pool 2. Verify pool size in Consul config 3. Compare against peak traffic 4. Bump pool to 50, restart pod Root cause: Redis pool exhaustion during peak Took 45 min to resolve DrDroid captures this pattern 2 weeks later Same pattern reappears — Agent runs it automatically ALERT: payment-svc p99 latency > 2s DrDroid Agent (auto) Checked redis-payments-03 pool Confirmed pool exhaustion at peak Bumped pool to 50, restarted pod Resolved automatically No human involved Resolved in 90 seconds
Cost Intelligence

Surface cost savings across your stack

Overprovisioned resources and idle infrastructure waste money — but finding them requires checking across clusters, clouds, and tools.

Because DrDroid maps your entire infrastructure, it can identify savings holistically — from right-sizing pods to cleaning up unused resources across providers.

Watch how it works
Cost Optimization Report Monthly savings found $4,280 Recommendations 12 Resources analyzed 847 $ Right-size 4 over-provisioned EC2 instances -$1,840/mo $ Remove 3 unused EBS volumes (90+ days idle) -$960/mo $ Switch 2 RDS instances to reserved pricing -$1,480/mo Scanned automatically — updated weekly
Monitoring Health

Keep dashboards and alerts in sync with reality

Dashboards and alerts go stale as infrastructure evolves — new services ship without monitoring, old alerts fire for things that no longer exist.

The agent knows what's actually running and what's being monitored. It flags gaps, retires stale alerts, and suggests coverage for new services — keeping your observability aligned with your real infrastructure.

Dashboard & Alert Improvement Before 12 stale alerts (no triggers in 30d) 3 dashboards with missing panels No coverage for new auth-service 5 duplicated alert rules After DrDroid 12 stale alerts retired 3 dashboards auto-repaired auth-service alerts created 5 duplicates merged into 2 Runs weekly — keeps you current
Accelerated debugging | Fewer escalations | Tribal knowledge preserved

Connects to 80+ tools your team already uses

80+ tools that the Agent knows how to use, from Kubernetes to Grafana to Github to custom internal tools.

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Need something custom?

Add your own integrations — custom MCP servers, custom CLIs, and custom skills — so the agent works with your internal tools too.

See how teams are using DrDroid in production

Frequently Asked Questions

Everything you need to know about DrDroid

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