Product Catalog

RE

Relvy

Hey HN! We are Bharath, and Simranjit from Relvy AI (https://www.relvy.ai). 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 in minutes. Here’s a video: [[[https://www.youtube.com/watch?v=BXr4_XlWXc0]]] 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. How it works: Relvy is installed on a 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. You can also configure AWS CLI commands that Relvy can run to automate mitigation actions, with human approval. A little bit about us - We did YC back in fall 2024. 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. Give us a try today. Happy to hear feedback, or about how you are tackling on-call burden at your company. Appreciate any comments or suggestions!

WS

We scored 50k PRs with AI

I'm a CTO with a ~16-person engineering team. Last year I wanted real data on what was actually shipping, not guesswork or story point theater. So we built GitVelocity. Every merged PR gets scored 0–100 by Claude across six dimensions: scope (0–20), architecture (0–20), implementation (0–20), risk (0–20), quality (0–15), perf/security (0–5). Six dimensions added up, then scaled by change size — a 10-line fix scores lower than a 500-line refactor even at the same complexity. Full formula at gitvelocity.dev/scoring-guide. After scoring 50,000+ PRs across TypeScript, Python, Rust, Go, Java, Elixir, and more, some things surprised us: Big PRs don't automatically score high. An 800-line migration with low complexity scores worse than a 200-line architectural change. Size gets you the full multiplier, but the base score still has to earn it. You can't score well without tests. The quality dimension (0–15) won't give you points without test coverage. At similar experience levels, this was the clearest separator between engineers. Juniors started outscoring some seniors. They adopted AI tools faster and took on harder problems. Once they could see their own scores, they aimed higher. We score AI-generated code the same as human-written code. Code is code. An engineer who uses AI to ship more complex work faster is more productive, and their scores reflect that. Scoring consistency was the hardest technical problem. Without reference examples anchoring each dimension, Claude's scores drifted 15+ points between runs. With 18 calibrated anchors (three per dimension at low/mid/high), we got it down to 2–4 points on the same PR. The thing we didn't expect was behavioral. We call it the Fitbit effect — the tool doesn't make you ship better code, but seeing the score does. Engineers started referencing their own scores in 1:1s unprompted, because the numbers matched what they already felt about their work. A junior who shipped a tricky concurrency fix could point to a score that proved it wasn't "just a small PR." We recently added team benchmarks (gitvelocity.dev/demo/benchmarks). Once you're scoring PRs, you can see how your team compares to others across the dataset — about 1,000 engineers on 60 teams so far. Headline's team ships faster than roughly 95% of them, which was nice to confirm but also made us wonder who the other 5% are. The competitive angle surprised us: teams that were skeptical about individual scores got genuinely curious once they could measure themselves against the field. Every score is fully visible to the engineer who wrote the PR, with per-dimension breakdowns and reasoning. There's no hidden dashboard that management sees and engineers don't. Free, BYOK (your Anthropic API key). We default to Sonnet 4.6, which scores nearly as well as Opus 4.6 at a fraction of the cost — but you can switch models if you want. Pennies per PR either way. No source code stored, diffs analyzed and discarded. Works with GitHub, GitLab, and Bitbucket. Ask me anything about the scoring methodology, how we solved calibration, or what it was actually like rolling this out to a team.