# Relvy AI: debugging notebooks for incident response

Canonical: https://mudpie.ai/companies/relvy-ai/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Relvy AI: debugging notebooks for incident response](https://mudpie.ai/companies/relvy-ai/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

Relvy AI is building an incident-response layer for software teams that are tired of turning alert noise into an on-call archaeology project. Its product is an AI debugging notebook that connects to observability data, investigates an issue and leaves a root-cause trail engineers can correct.

## What it does

The [YC profile](https://www.ycombinator.com/companies/relvy-ai) says Relvy finds the root cause in more than 70% of alerts automatically, while letting engineers guide or correct the investigation when it misses. The [YC launch](https://www.ycombinator.com/launches/MGE-relvy-ai-that-discovers-and-debugs-production-issues) describes a setup where teams connect observability tools, provide log queries, and let Relvy monitor and debug continuously. It can also investigate alerts already arriving in an on-call Slack channel and identify responsible code changes from recent commits.

The buyer is a software team with enough services, alerts and deploys that engineers spend time deciding which signal matters before debugging can start. Relvy says its small debugging model matches GPT-4o on internal evals at a fraction of the cost; that is a company-reported benchmark, not a production SLO guarantee.

## Why I’d look closer

The advantage is the notebook model. An investigation can preserve queries, context, hypotheses and corrections instead of producing another one-off AI answer. Founder context fits the problem: Bharath Bhat worked on computer-vision and language models at Google Pixel, YouTube and Uber; Simranjit Singh worked on computer vision at Microsoft and Amazon and distributed systems at Nutanix.

The tradeoff is access and trust. Root-cause analysis needs logs, metrics, traces, commits and sometimes sensitive customer context. A confident explanation that misses the actual failure can delay recovery, and an agent that proposes a code change must remain outside the deployment path until reviewed.

## What I’d ask

Which logs, metrics and repositories can Relvy read? Can engineers inspect the evidence behind each cause and correct the investigation? How are secrets, customer data and incident transcripts retained? What happens when telemetry is incomplete or two changes plausibly caused the alert? Does the system learn from corrections without leaking one team’s incidents to another?

## My editorial take

Shortlist Relvy if incident triage is taking more time than the actual fix. Start with read-only investigation on one service, compare time-to-understand and false-cause rates with the existing process, and keep code or deployment changes human-approved. The product’s value is accumulated incident context, not an AI sentence that sounds decisive.

## Quick facts

| Field | Sourced detail |
| --- | --- |
| Product | AI debugging notebooks, log monitoring and root-cause investigation |
| Buyer | Software engineering, SRE and on-call teams |
| Company claims | Root cause on 70%+ of alerts; internal small-model eval near GPT-4o |
| Pricing | Not published in the checked pages |
| Main question | Does the investigation reduce alert noise without hiding its evidence? |

## Sources checked

| Source | Checked |
| --- | --- |
| [YC company profile](https://www.ycombinator.com/companies/relvy-ai) | 2026-09-19 |
| [Relvy YC launch](https://www.ycombinator.com/launches/MGE-relvy-ai-that-discovers-and-debugs-production-issues) | 2026-09-19 |
| [Relvy website](https://www.relvy.ai/) | 2026-09-19 |

## Cohort context

Relvy AI is listed in Fall 2024. In our 2026-09-18 directory snapshot, 57 of 94 listed companies in that cohort have YC’s primary industry label B2B (60.6%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:14:42.730Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Not observed in this response |
| H1 or H2 heading | Not observed in this response |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.