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ReplyArgus
Every app review you ignore is doing damage. Answer them all
Reply Argus reads your store listings and past approved replies to learn your app and your voice, then drafts on-brand answers to every App Store and Google Play review in the reviewer's own language. Every approval sharpens it. Auto-draft everything and auto-publish only when you trust. Its priority signal feed ranks recurring complaints by what to fix first, each tied to the reviews behind it. Supports integrations with all your favorite agentic tools. Integrate it in your apps as needed.
I built this after a painful mistake with my own app: I ignored my reviews, 1-stars piled up, and my store visibility dropped because Apple and Google now index that feedback. Trying to catch up manually, translating languages, matching tone, and remembering which bugs were fixed was an exhausting task. Existing AI tools just felt like generic drafting assistants, but I wanted a system to actually take over the desk.
Here’s what makes ReplyArgus different: it learns your app.
Connect your store, and it autonomously ingests your marketing pages to build a custom knowledge base. It drafts native-language replies grounded in your actual features and past approved answers. Every edit you make sharpens the model. You start by approving drafts, and as it earns your trust, you can switch to auto-publish.
Three features I didn't expect to build, but absolutely love:
Context Drift Detection: It automatically spots discrepancies between the information on your marketing page, your app store description, and any custom data you've added. It parks those conflicting insights for your manual review so your replies never contradict your actual positioning.
Smart Feedback Clustering: It groups recurring feedback into evidence-backed priority signals that you can export whenever you need them. Your reviews literally tell you what to focus on building next.
Built-in MCP Server: I've baked in a remote MCP server. You can manage your whole review desk, securely scoped to your projects added. Pass in feedbacks via a regular sync as needed to it to fix it.
ReplyArgus is still evolving, and I'd love your help shaping it. Please let me know what you think, and drop any questions you have below.
About ReplyArgus on Product Hunt
“Every app review you ignore is doing damage. Answer them all”
ReplyArgus was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #149 on the daily leaderboard. Reply Argus reads your store listings and past approved replies to learn your app and your voice, then drafts on-brand answers to every App Store and Google Play review in the reviewer's own language. Every approval sharpens it. Auto-draft everything and auto-publish only when you trust. Its priority signal feed ranks recurring complaints by what to fix first, each tied to the reviews behind it. Supports integrations with all your favorite agentic tools. Integrate it in your apps as needed.
On the analytics side, ReplyArgus competes within Customer Success, Developer Tools and Artificial Intelligence — topics that collectively have 999.5k followers on Product Hunt. The dashboard above tracks how ReplyArgus performed against the three products that launched closest to it on the same day.
Who hunted ReplyArgus?
ReplyArgus was hunted by Joel Jaimon. 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 ReplyArgus including community comment highlights and product details, visit the product overview.
Joel Jaimon
Hey Product Hunt,
I'm the solo maker behind ReplyArgus.
I built this after a painful mistake with my own app: I ignored my reviews, 1-stars piled up, and my store visibility dropped because Apple and Google now index that feedback. Trying to catch up manually, translating languages, matching tone, and remembering which bugs were fixed was an exhausting task. Existing AI tools just felt like generic drafting assistants, but I wanted a system to actually take over the desk.
Here’s what makes ReplyArgus different: it learns your app.
Connect your store, and it autonomously ingests your marketing pages to build a custom knowledge base. It drafts native-language replies grounded in your actual features and past approved answers. Every edit you make sharpens the model. You start by approving drafts, and as it earns your trust, you can switch to auto-publish.
Three features I didn't expect to build, but absolutely love:
Context Drift Detection: It automatically spots discrepancies between the information on your marketing page, your app store description, and any custom data you've added. It parks those conflicting insights for your manual review so your replies never contradict your actual positioning.
Smart Feedback Clustering: It groups recurring feedback into evidence-backed priority signals that you can export whenever you need them. Your reviews literally tell you what to focus on building next.
Built-in MCP Server: I've baked in a remote MCP server. You can manage your whole review desk, securely scoped to your projects added. Pass in feedbacks via a regular sync as needed to it to fix it.
ReplyArgus is still evolving, and I'd love your help shaping it. Please let me know what you think, and drop any questions you have below.