Sherlocks.ai helps SRE and engineering teams cut production incident investigation time by automating triage, root cause analysis, signal correlation, and escalation context across logs, metrics, traces, deployments, infrastructure, and Slack.
Engineers should not have to spend hours manually debugging production incidents across alerts, logs, metrics, traces, dashboards, deployments, infrastructure changes, Slack threads, and prior RCAs.
Sherlocks is an AI SRE platform that helps teams cut production incident investigation time by automatically triaging alerts, correlating operational signals, finding likely root cause, building an incident timeline, and giving engineers the context they need to resolve production incidents faster.
Production incidents are rarely slow because teams lack alerts. They are slow because alerts do not explain what changed, which service is affected, what evidence matters, or where the investigation should start.
During an incident, engineers often have to check observability dashboards, query logs, inspect traces, compare recent deployments, review infrastructure state, search Slack threads, and reconstruct the timeline by hand.
That repetitive investigation work increases MTTR, pulls senior engineers into every incident, and keeps teams searching for root cause instead of fixing the issue.
Sherlocks.ai reduces that manual loop by automating the first pass of incident triage, signal correlation, root cause investigation, and escalation context.
Sherlocks.ai acts as an AI SRE investigation layer for production incidents. When an alert fires, Sherlocks can start an investigation automatically, collect context from connected systems, query telemetry sources, analyze dependencies, and generate an evidence-backed RCA.
Instead of only notifying engineers that something is wrong, Sherlocks helps answer:
The goal is not to replace engineers. It is to reduce engineering time spent investigating incidents so teams can spend less time debugging production failures and more time resolving them.
Sherlocks uses its Awareness Graph to connect architecture, telemetry, deployment history, incident memory, Slack context, and team knowledge. It can correlate incident signals from logs, metrics, traces, alerts, Kubernetes state, cloud infrastructure, databases, queues, CI/CD pipelines, source code, commits, Slack incident threads, prior RCAs, and postmortems.
This helps Sherlocks.ai connect “what changed” with “what broke” without forcing engineers to switch across disconnected tools during an incident.
Sherlocks integrates with observability, logging, APM, cloud, Kubernetes, database, queue, CI/CD, code, and collaboration systems, including:
Sherlocks helps reduce time to root cause by automatically generating, testing, and ranking likely causes against available incident data.
A Sherlocks RCA can include:
Sherlocks supports AI-driven root cause analysis for application errors, slow APIs, infrastructure failures, Kubernetes crash loops, queue backlogs, database problems, replication lag, long-running queries, CI/CD failures, deployment mistakes, and configuration errors.
This helps teams speed up incident diagnosis without relying on a senior engineer to manually inspect every system.
Sherlocks shortens production troubleshooting by automatically collecting the evidence engineers normally search for manually.
Instead of starting with a blank dashboard and a noisy alert, responders can start with a working hypothesis, supporting evidence, impacted services, likely blast radius, and suggested next steps.
Sherlocks helps engineering teams spend less time debugging production failures and more time fixing them. It automates the first-pass investigation work that usually requires engineers to check dashboards, query logs, compare deployments, inspect traces, review Slack threads, and manually reconstruct the incident timeline.
This helps teams reduce investigation overhead during incident response, reduce operational toil, and investigate before escalating to senior engineers.
Sherlocks is especially useful when production support depends on tribal knowledge from the engineer who built the service. Its Awareness Graph can reuse past investigations, Slack incident conversations, runbook references, deployment correlations, service dependencies, prior RCAs, and known failure patterns so future responders do not have to start from zero.
Sherlocks is built around the part of incident response that most directly affects MTTR: the time between alert and credible root-cause hypothesis.
API slowdown RCA reduced from 2 hours to 5 minutes, Kubernetes crash-loop root cause identified in seconds, and MTTR improved from 3.5 hours to 22 minutes.
Sherlocks is not just another alerting tool.
Alerting tools notify teams that something is wrong. Observability tools expose logs, metrics, traces, and dashboards. Incident management tools help coordinate response, ownership, escalation, and communication.
Sherlocks focuses on the investigation layer. It helps teams understand what likely caused the incident, what evidence supports the hypothesis, which services are affected, what changed recently, and what actions engineers should consider next.
That makes Sherlocks.ai complementary to existing observability, alerting, and incident response workflows.
Sherlocks supports enterprise deployment, security, and privacy requirements for teams that need stronger control over incident data and AI infrastructure.
Deployment options include:
Security and operational controls include read-only permissions for Watson, encryption in transit and at rest, separate encryption keys per customer, key rotation policies, and configurable retention and deletion controls. Watson cannot modify infrastructure, databases, queues, credentials, secrets, or application data.
Sherlocks is designed for engineering, SRE, DevOps, and support teams that want to reduce manual incident investigation and speed up production troubleshooting.
Sherlocks automatically gathers context from connected systems, correlates logs, metrics, traces, deployments, infrastructure metadata, Slack context, and prior incidents, then generates an RCA with likely root cause, timeline, affected services, blast radius, evidence links, and recommended next actions.
Yes. Sherlocks helps reduce time to root cause by generating and testing RCA hypotheses against logs, metrics, traces, deployments, infrastructure state, Slack context, and prior incident memory.
Sherlocks automates the first pass of production troubleshooting by collecting incident evidence, correlating signals, reconstructing timelines, identifying likely causes, and giving engineers a starting point before they manually debug the issue.
No. Sherlocks works with observability and infrastructure tools by querying and correlating their data during incident investigation. Observability tools show raw telemetry; Sherlocks helps interpret that telemetry in the context of a production incident.
Sherlocks is better understood as an AI investigation layer that can work alongside alerting and incident response tools. PagerDuty helps notify and coordinate response; Sherlocks helps investigate what likely caused the incident and what evidence supports that finding.
Yes. Sherlocks is Slack-native and can deliver investigation summaries, timelines, evidence links, impacted services, recommendations, and follow-up answers directly in Slack.
No. Sherlocks is designed with read-only permissions and safety controls. It can suggest remediation steps, but critical changes require approval.
Automate alert triage, production incident investigation, and root-cause analysis across logs, metrics, traces, deployments, infrastructure, Slack, and historical incidents.
Learn how Sherlocks.ai automatically investigates production incidents, correlates telemetry and recent changes, and produces evidence-backed RCA in Slack.
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