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Modulis is a BYOC managed production resolution platform. It correlates logs, metrics, traces, errors, session replays, and Git metadata, with AI-generated root cause analysis and PR-ready code fixes in minutes.
We built Modulis because debugging production incidents is still too fragmented. Engineering teams often have to jump between logs, traces, errors, user sessions, and Git history to understand what went wrong and identify the actual root cause.
We wanted to bring that context together and use AI to help engineers investigate incidents and generate PR-ready fixes that developers can review and approve.
Our goal is to make production debugging faster without taking control away from engineers.
The feedback we'd value most:
Does Modulis fit the way your team currently investigates production incidents?
What information or integrations would make root-cause analysis more useful?
How much would you trust AI-generated fixes, and what would you need to review before merging them?
What security, deployment, or data-control requirements would you expect from a tool like this?
We're especially interested in honest feedback from developers, SREs, DevOps engineers, and engineering leaders. We want to build something that solves a real problem in their day-to-day workflow.
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About Modulis on Product Hunt
“opens a pull request with the fix”
Modulis was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #337 on the daily leaderboard. Modulis is a BYOC managed production resolution platform. It correlates logs, metrics, traces, errors, session replays, and Git metadata, with AI-generated root cause analysis and PR-ready code fixes in minutes.
Modulis was featured in Software Engineering (43.3k followers), Developer Tools (522.6k followers) and Artificial Intelligence (483k followers) on Product Hunt. Together, these topics include over 235.8k products, making this a competitive space to launch in.
Who hunted Modulis?
Modulis was hunted by Suprith KG. 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 Modulis stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Suprith KG
We built Modulis because debugging production incidents is still too fragmented. Engineering teams often have to jump between logs, traces, errors, user sessions, and Git history to understand what went wrong and identify the actual root cause.
We wanted to bring that context together and use AI to help engineers investigate incidents and generate PR-ready fixes that developers can review and approve.
Our goal is to make production debugging faster without taking control away from engineers.
The feedback we'd value most:
Does Modulis fit the way your team currently investigates production incidents?
What information or integrations would make root-cause analysis more useful?
How much would you trust AI-generated fixes, and what would you need to review before merging them?
What security, deployment, or data-control requirements would you expect from a tool like this?
We're especially interested in honest feedback from developers, SREs, DevOps engineers, and engineering leaders. We want to build something that solves a real problem in their day-to-day workflow.