Relvy is a specialized AI agent that investigates on-call alerts autonomously. Compared to general purpose agents (Claude Code + Datadog MCP), Relvy is more accurate and cheaper per alert, and each investigation produces a notebook with rich visualizations for engineers to review and build trust with the AI. Teams in fintech and consumer software use Relvy as the first line of defence on their on-call Slack channels. SOC 2 Type II compliant with self-host options. Setup takes under 15 minutes.
On-call was the single biggest source of engineer burnout on my team when I was CTO scaling from 15 to 120 engineers. The problem was never that people couldn't diagnose issues - it was that the investigation step ate all the cognitive energy. An engineer gets paged at 2 AM, spends 40 minutes just figuring out which service is actually broken, and by the time they identify the root cause they're too fried to think clearly about the fix. The fact that Relvy produces a notebook with the full investigation trail is the right design choice because even when the AI doesn't nail the root cause perfectly, having all the relevant logs, metrics, and traces pre-gathered in one place cuts that investigation phase from 40 minutes to 5. That alone would have saved my team hundreds of hours per quarter. How are you handling the cold start problem - when Relvy connects to a new codebase it hasn't seen before, how quickly does it get useful at correlating service dependencies?
Hey folks, this is Bharath, one of the founders of Relvy.
A lot of teams are using AI 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 for this: (1) Telemetry data volume can drown the model in noise; (2) Data interpretation / reasoning is enterprise context dependent; (3) 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 specialized 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 leads to less agentic exploration and more deterministic steps that reflect the most useful steps that an experienced engineer would take. That results in faster analysis, and less cognitive load on engineers to review and understand what the AI did.
Give Relvy a try today - you can run it entirely on your local machine, connected to your LLM of choice, with no data shared with Relvy. Appreciate any comments or suggestions!
About Relvy on Product Hunt
“Your AI On-call Engineer”
Relvy launched on Product Hunt on April 28th, 2026 and earned 72 upvotes and 4 comments, placing #48 on the daily leaderboard. Relvy is a specialized AI agent that investigates on-call alerts autonomously. Compared to general purpose agents (Claude Code + Datadog MCP), Relvy is more accurate and cheaper per alert, and each investigation produces a notebook with rich visualizations for engineers to review and build trust with the AI. Teams in fintech and consumer software use Relvy as the first line of defence on their on-call Slack channels. SOC 2 Type II compliant with self-host options. Setup takes under 15 minutes.
Relvy was featured in Software Engineering (42.4k followers), Developer Tools (511.7k followers) and Artificial Intelligence (467.2k followers) on Product Hunt. Together, these topics include over 162.9k products, making this a competitive space to launch in.
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.
Want to see how Relvy stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Garry Tan
SOC 2 self hosting is a strong combo especially for fintech teams. That alone removes a big barrier to adoption.