Wraith is an AI‑driven platform that ingests telemetry and alert streams to detect anomalies, correlate events, and suppress noise in real time. It automatically performs root‑cause analysis, generates remediation code patches with accompanying tests, and submits them to version‑control systems for deployment via existing CI/CD pipelines. The service integrates with common observability and incident‑management tools, helping SRE and DevOps teams reduce MTTR and alert fatigue.
Funding
Funding not disclosed
Founders
Product
Problem
Production environments generate high volumes of alerts, many of which are duplicates or low‑signal noise, leading to frequent on‑call paging, long mean time to resolution (MTTR), and engineer burnout. Teams also spend considerable manual effort on root‑cause analysis and writing remediation code under pressure.
Solution
Wraith provides an AI‑driven on‑call engineer that continuously ingests telemetry and alert streams, applies statistical and machine‑learning models to detect anomalies, and correlates related events to suppress noise. When an incident is identified, the platform automatically builds a dependency graph, performs root‑cause analysis, and generates a remediation pull request that includes context‑aware code changes and accompanying unit or integration tests. The fix is submitted to the team's version‑control system for review, and deployment can proceed through existing CI/CD pipelines, eliminating the need for manual paging. By automating detection, analysis, and remediation, Wraith reduces MTTR to seconds, cuts alert fatigue, and frees engineers to focus on higher‑value work. The service is delivered via a web dashboard and API that integrate with common observability tools and incident‑management platforms.
Target Audience
The primary customers are SRE and DevOps teams managing microservice‑based SaaS, fintech, or e‑commerce platforms that require continuous reliability and rapid incident response.
Features
- Real‑time anomaly detection using hybrid statistical thresholds and supervised ML models on metrics, logs, and traces
- Alert correlation engine that de‑duplicates and suppresses noise, presenting a single incident view
- Automated dependency mapping and root‑cause analysis with graph traversal of service topology
- AI‑generated code patches (e.g., retry logic, timeout adjustments) with context‑aware diff creation via GitHub/GitLab APIs
- Auto‑generated test suites (unit and integration) to validate the remediation before merge
- Seamless integration with observability stacks (Prometheus, Datadog, OpenTelemetry) and incident platforms (PagerDuty, Opsgenie) through RESTful APIs
- Dashboard displaying incident timeline, MTTR metrics, and remediation status, with role‑based access controls and audit logging