Provides an AI-powered incident management platform that integrates with tools like Datadog, GitHub, and Slack to automate root cause analysis and real-time incident response. By dynamically querying logs, assessing customer impact, and identifying deployment-related issues, it reduces mean time to resolution (MTTR), minimizes alert fatigue, and prevents on-call engineer burnout.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Modern software systems are increasingly complex, leading to a surge in production alerts that overwhelm on-call engineers. Identifying the root cause of incidents quickly is challenging due to the vast amounts of data and the need to correlate information across disparate systems. This often results in delayed incident resolution, engineer burnout, and potential SLA violations.
Solution
Vespper is an AI-powered incident management platform designed to automate root cause analysis and streamline real-time incident response. By integrating with existing observability tools, collaboration platforms, and issue trackers, Vespper dynamically queries logs, assesses customer impact, and identifies deployment-related issues. The platform acts as an AI on-call engineer, providing contextual findings and surfacing relevant data to help engineers troubleshoot incidents more efficiently. Vespper's multi-agent system is triggered by alerts, sifts through data, and presents the most pertinent information, reducing mean time to resolution (MTTR) and minimizing alert fatigue.
Target Audience
Vespper is designed for DevOps teams, SREs, and on-call engineers in organizations that deal with strict SLAs, experience frequent outages, or are growing rapidly and need to ensure no alerts fall through the cracks.
Features
- Seamless integration with popular observability tools like Datadog, Coralogix, Opsgenie, and Pagerduty
- Connects to collaboration platforms such as Slack, GitHub, Notion, Jira, and Confluence for comprehensive incident analysis
- Automated troubleshooting of alerts and incidents in real-time
- Dynamic log querying to identify anomalies and potential root causes
- Automatic assessment of customer impact to prioritize incidents effectively
- Identification of deployment-related issues to pinpoint recent changes that may have triggered the incident
- Open-source AI copilot that allows users to interact with observability data and code
- Terraform support for easy deployment on AWS