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Relvy
Your AI On-call Engineer
Relvy automates on-call runbooks for software engineering teams. It is an AI agent equipped with tools that can analyze telemetry data and code at scale, helping teams debug and resolve production issues faster. Relvy can detect anomalies and identify problem slices from dense time series data, do log pattern search, and reason about span trees, all without overwhelming the agent context. Engineers can run Relvy locally on their machines - set up takes less than 15 minutes.
Hey PH, this is Bharath, one of the founders of Relvy.
A lot of teams are using AI today in some form to reduce their on-call burden. You may be pasting logs into Cursor, or using Claude Code with Datadog’s MCP server to help debug. What we’ve seen is that autonomous root cause analysis is a hard problem for AI. This shows up in benchmarks - Claude Opus 4.6 is currently at 36% accuracy on the OpenRCA dataset, in contrast to coding tasks. There are three main reasons:
- Telemetry data volume can drown the model in noise.
- Data interpretation / reasoning is enterprise context dependent.
- On-call is a time constrained, high stakes problem, with little room for AI to explore during investigation time. Errors that send the user down the wrong path are not easily forgiven.
At Relvy, we are tackling these problems by:
- Building custom tools for telemetry data analysis - our tools can detect anomalies and identify problem slices from dense time series data, do log pattern search, and reason about span trees, all without overwhelming the agent context.
- Anchoring the agent around runbooks - Less agentic exploration, more deterministic steps that reflect the most useful steps that an experienced engineer would take. The result - fast analysis, and less cognitive load on engineers to review and understand what the AI did.
Here’s how Relvy works:
- Installed on a user’s local machine via docker-compose (or via helm charts, or sign up on our cloud)
- Connect your stack (observability and code)
- Create your first runbook and have Relvy investigate a recent alert.
- Each investigation is presented as a notebook in our web UI, with data visualizations that help engineers verify and build trust with the AI.
- From there on, Relvy can be configured to automatically respond to alerts from Slack
Some example runbook steps that Relvy automates:
- Check so-and-so dashboard, see if the errors are isolated to a specific shard.
- Check if there’s a throughput surge on the APM page, and if so, is it from a few IPs?
- Check recent commits to see if anything changed for this endpoint.
- [Limited support for mitigation steps] - You can also configure aws cli commands that Relvy can run to automate mitigation actions, with human approval.
A little bit about us - We started our journey experimenting with continuous log monitoring with small language models - that was too slow. We then invested deeply into solving root cause analysis effectively, and our product today is the result of about a year of work with our early customers.
Thanks!
About Relvy on Product Hunt
“Your AI On-call Engineer”
Relvy launched on Product Hunt on April 14th, 2026 and earned 67 upvotes and 1 comments, placing #38 on the daily leaderboard. Relvy automates on-call runbooks for software engineering teams. It is an AI agent equipped with tools that can analyze telemetry data and code at scale, helping teams debug and resolve production issues faster. Relvy can detect anomalies and identify problem slices from dense time series data, do log pattern search, and reason about span trees, all without overwhelming the agent context. Engineers can run Relvy locally on their machines - set up takes less than 15 minutes.
On the analytics side, Relvy competes within Software Engineering, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Relvy performed against the three products that launched closest to it on the same day.
Who hunted Relvy?
Relvy was hunted by Garry Tan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Relvy including community comment highlights and product details, visit the product overview.
Garry Tan