Sherlocks investigates across data sources to deliver RCA in minutes.
Seamless Tool Integration
Debug 10x Faster
Proactive Incident PreventionSherlocks answers it in minutes, with the command output behind each finding.
$ kubectl describe podLast State: Terminated Reason: OOMKilled Exit Code: 137 10:34:59Z show_manual_review_kyc10:35:02Z KycStatusLog: 22,364,96410:39:43Z OOMKilled
An unbounded KYC query pulled 22.36M rows into a 2.5 GB limit
Sherlocks reads the stack you already run. No re-instrumentation, no migration.
| APM | |
|---|---|
| Cloud providers |  AWS |
| Metrics and logs | |
| Dashboards and tracing | |
| Databases | |
| CI/CD and version control | |
| Streaming and messaging | Amazon SQS |
Built to meet rigorous security, availability, and confidentiality standards.
Sherlocks AI runs entirely inside your Virtual Private Cloud, ensuring data never leaves your controlled environment.
All interactions, from API calls to stored information, are fully encrypted for end-to-end protection.
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What engineering teams ask us most about running an AI SRE in production.
Sherlocks AI is built for teams that need on-call coverage without hiring more SREs. It acts as an AI SRE teammate that investigates incidents 24/7 and delivers root cause analysis in minutes rather than hours, cutting MTTR by up to 10x. Small teams can start free with 30 investigations per month, self-serve onboarding, and no credit card. It runs in Slack, so there is no new tool for the team to learn, and it is SOC 2 Type 2 certified and VPC-native, so it clears security review even at lean companies selling into enterprises.
Sherlocks AI has a Free plan at $0 with 30 investigations per month, all 16+ AI agents, Slack and Microsoft Teams integration, and self-serve onboarding, with no credit card required. The Enterprise plan is custom-priced and adds unlimited investigations, a dedicated field engineer for setup, a support engineer with an SLA, SSO/SAML, audit logs, RBAC, and air-gapped or in-VPC deployment options. An investigation is counted when Sherlocks receives an alert and dispatches agents to analyze it; correlated alerts in the same incident window count as one.
Most alerts are analyzed in two to three minutes, with complex multi-service incidents taking five to six. Sherlocks maps the alert to a knowledge graph of your infrastructure, forms a hypothesis, queries logs, metrics, and traces across your stack to validate it, then reports the root cause along with short-term and long-term remediations. Teams typically see MTTR fall from hours to minutes, alert noise drop by 90%, and downtime drop by 70%.
No. Sherlocks AI complements your observability tools rather than replacing them. It integrates with Datadog, New Relic, Prometheus, Sentry, Coralogix, Elasticsearch, and Loki, alongside AWS, GCP, Azure, Kubernetes, PostgreSQL, MySQL, MongoDB, Redis, Kafka, RabbitMQ, GitHub Actions, and Jenkins. Because the Watson agent reads directly from your infrastructure as well as your monitoring tools, Sherlocks can still investigate in places where your dashboards have blind spots.
Yes. Sherlocks AI is SOC 2 Type 2 certified. The Watson data agent runs inside your own VPC with strictly read-only permissions, so raw telemetry never leaves your controlled environment, and all interactions from API calls to stored information are encrypted in transit and at rest. Regulated customers can run fully air-gapped deployments and keep RCA processing inside their own cloud, and the security team is available to walk through compliance requirements directly.
Most teams finish setup in under 30 minutes and see their first AI-led investigation within an hour. You deploy the Watson collector into your cloud account using the Terraform module, Helm chart, or CloudFormation template, connect your existing integrations, and route your alerts to it. Sherlocks then builds a knowledge graph of your infrastructure and applications automatically, with minimal human input. Kubernetes is not required.
A general-purpose agent can reach your observability APIs and run commands, but you still have to wire it up, prompt it, and steer it — at 3 a.m., during an incident. Sherlocks AI is triggered by the alert itself and investigates autonomously with 16+ domain-specialized agents, then reports back in Slack. It also carries institutional memory across infrastructure, past incidents, runbooks, and Slack conversations, so each investigation starts with the context your team has already accumulated instead of from a blank prompt.
The product is Sherlocks AI, plural, at sherlocks.ai. Many people search for the singular "Sherlock AI" and mean this same platform: the same company, the same 16+ specialized agents, the same autonomous incident investigation and root cause analysis in Slack. The name is plural on purpose, because an incident is investigated by a team of detectives working in parallel rather than by a single one. The singular term is also used by several unrelated products, so searching for "Sherlocks AI" or "Sherlocks AI SRE" is the reliable way to find us.
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