Automated Root Cause Analysis for Production Incidents, in Minutes
Sherlocks investigates logs, metrics, traces, and deployments the moment an alert fires, then delivers an evidence-backed root cause in Slack or Microsoft Teams.
Deploy v2.14.0 dropped the DB_POOL_MAX override. orders-db hit 100/100 connections and checkout requests queued behind it.
- checkout-api v2.14.0 deployed
- orders-db connection pool saturated
- p99 latency alert fired
- RCA delivered in Slack
- Connection pool exhausted after deploy High
- Slow query on orders table Contributing
- Node CPU pressure Ruled out
AI RCA Software That Hands You a Diagnosis, Not an Alert Summary
Alerts say something is wrong. Sherlocks says why, with the proof attached.
- Collects relevant production evidence
- Maps affected dependencies
- Tests every root-cause hypothesis
What your on-call gets in every automated RCA
- Primary suspected root cause
- Confidence level
- Supporting logs, metrics, and traces
- Incident timeline
- Recent deployments and commits
- Contributing factors
- Affected services and blast radius
- Recommended mitigation
- Recurrence-prevention actions
- Links to the underlying evidence
Automated Root Cause Analysis That Finds the Origin, Not the Symptom
Production failures rarely stay inside one system. The service that alerts is rarely the service that broke.
Root cause analysis across your entire production stack
Downstream alert, traced to the originating failure
How Sherlocks Automates Production Incident Investigation
-
01
Any production alert starts an investigation
No one has to wake up first
-
02
16+ AI agents correlate telemetry, infrastructure & recent changes
Dashboard-hopping, done in parallel
-
03
Root-cause hypotheses tested and ranked
Ranked by confidence and impact
-
04
Evidence-backed RCA lands in Slack or Teams
Ask follow-ups or go deeper in-thread
Evidence-Backed AI Root Cause Analysis Your Engineers Can Trust
Sherlocks shows the work, so engineers verify every conclusion instead of second-guessing a black box.
Every finding links to its evidence
- Relevant log lines
- Metric anomalies
- Distributed traces
- Infrastructure resources
- Kubernetes events
- Deployments and CI/CD runs
- Commits and pull requests
- Dashboards
- Similar incidents
- Supporting command output
Confidence on every conclusion
Primary suspected cause
High confidenceThe originating failure, with supporting evidence
Contributing factors
MediumConditions that made the failure worse or more likely
Downstream effects
SymptomsServices that alerted because of the root cause
AI Incident Investigation Software for SRE and DevOps Teams
Whatever broke, your team starts with an answer, not a blank screen.
Deploys & code
- Bad deployments
- Code regressions
Databases
- Database latency
- Slow queries and deadlocks
- Connection saturation
Kubernetes & cloud
- Kubernetes scheduling and configuration failures
- Cloud infrastructure degradation
- Scaling and capacity issues
Resources & messaging
- Memory, CPU, network, and storage constraints
- Queue backlogs
- Messaging failures
Dependencies & alerts
- External dependency failures
- Multi-service outages
- Noisy or misconfigured alerts
Root Cause Analysis Automation vs. RCA Generators
RCA generators write up what engineers already figured out. Sherlocks automates the investigation itself.
| RCA generators | Sherlocks automated RCA |
|---|---|
Depend on pasted logs or notes | Collects evidence from connected production systems |
Summarize the information provided | Investigates telemetry, changes, topology, and history |
Generate a single explanation | Generates and tests competing hypotheses |
Focus on post-incident documentation | Supports live production incident investigation |
Provide limited source verification | Links conclusions to underlying evidence |
Produce static reports | Updates findings as new evidence appears |
Automated RCA That Works With Your Existing Observability Stack
Observability and logging
Cloud and infrastructure
Databases and messaging
CI/CD and incident workflows
Human-Reviewable RCA With Read-Only Production Access
Automation speed, without handing AI the keys to production.
Engineers stay in control
- Inspect the evidence behind every finding
- Ask follow-up questions
- Request deeper analysis
- Add evidence from inaccessible systems
- Introduce or reject hypotheses
- Review mitigation options
- Require approval for rollback recommendations
- Escalate to the right on-call owner
Watson infrastructure agent
Read-only, least-privilege access- Read telemetry, events & configAllowed
- Deploy changesBlocked
- Directly modify production systemsBlocked
- Rollback recommendationsYour approval
Built to clear security review
AI RCA That Learns From Previous Incidents
Never solve the same production failure from scratch twice.
Graph
What engineering teams get
Faster recognition of recurring failures
Reusable incident knowledge
More consistent investigations
Less dependence on individual engineers' memory
Better context for new team members
Daily incident and reliability reviews
Reduce Manual RCA Toil and Give Engineers Their Time Back
Sherlocks takes over repetitive investigation toil, not engineering judgment.
Manual RCA Before
With Sherlocks After
Automated RCA Platform FAQs
Can Sherlocks automatically investigate alerts?
Yes. Sherlocks can start an investigation when an alert fires, collect evidence from connected systems, test root-cause hypotheses, and deliver the findings in Slack, Microsoft Teams, or Sherlocks.
Does Sherlocks replace Datadog, Grafana, or other observability tools?
No. Sherlocks works across your existing observability stack, using telemetry and context from connected tools to automate the investigation that begins after an alert fires.
Can Sherlocks correlate incidents with deployments and code changes?
Yes. Sherlocks can correlate incidents with commits, pull requests, GitHub Actions workflows, Jenkins builds, Azure Pipelines runs, Kubernetes deployments, and Helm releases.
Can we verify Sherlocks' AI-generated RCA?
Yes. Sherlocks provides an evidence trail, links to relevant telemetry and resources, and a confidence level for the primary suspected cause. Engineers can challenge findings, add context, and request deeper investigation.
How long does it take to set up Sherlocks?
Most teams finish setup in under 30 minutes and see their first AI-led investigation within an hour. You deploy the Watson collector with the Terraform module, Helm chart, or CloudFormation template, connect your existing integrations, and route your alerts to Sherlocks. Kubernetes is not required.
Can Sherlocks run inside our environment?
Yes. Available deployment models include SaaS, an agent inside your VPC, hybrid deployment, fully in-VPC deployment, self-hosting, and air-gapped configurations.
Does Sherlocks automatically change production?
No. The Sherlocks Watson agent operates with read-only access and cannot directly modify production infrastructure. Remediation recommendations can be governed by your safety and approval policies.
Automate Your Next Root Cause Investigation
See exactly what your on-call would get, on a stack like yours.