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Optifeed Radar
Know if AI recommends your products. Open source, your keys.
Nobody asks an AI for the best brand. They ask for the best laptop, and the answer is a shelf of products. Optifeed Radar is an open-source CLI + MCP server that checks whether AI actually recommends YOUR products. Name them, and it asks real shopper questions that never mention them across ChatGPT, Gemini, Perplexity and Claude with your own API keys, then shows which products were never recommended and which rivals took the slot. Every score opens into the raw answer. Runs locally, MIT.
Hey Product Hunt! I'm Erdem, co-founder of Optifeed (the autopilot for ecommerce marketing). Optifeed Radar is our new open-source project, and it exists because buyers moved to asking AI what to buy, and merchants had no way to know if the engines ever mentioned them - short of asking one prompt at a time.
So it does what I did manually, in one command:
🔎 Generates the questions real buyers ask (none of them name your brand) 🤖 Sends them to ChatGPT, Gemini, Perplexity and Claude with YOUR API keys 📊 Scores mentions, position, sentiment, and share of voice vs competitors 🧾 Every score opens into the raw answer that produced it - no black box 🛒 Beta: name your products and see which ones AI never recommends, and which rivals it names instead
Honest notes, because you will ask: it spends your own API credit (a quick four-engine run measured about $0.41-0.46; the full default pack is roughly 2.5x that, which is what the demo video runs), scores are estimates from a sample and move between runs (there is a diff command instead of fake precision), and engine APIs are not identical to the consumer apps. It is MIT, runs locally, and it is also an MCP server, so your own AI agent can run the checks.
It is for developers, technical marketers, and agencies who want evidence instead of a dashboard subscription.
Special thanks to @nevo_david- his work and encouragement gave me a lot of inspiration and motivation to keep building and ship this.
I would love feedback on the scoring methodology (it is published in the repo) and on which engines to add next. I will be here all day.
npx optifeed-radar check yourbrandcom
About Optifeed Radar on Product Hunt
“Know if AI recommends your products. Open source, your keys.”
Optifeed Radar was submitted on Product Hunt and earned 11 upvotes and 3 comments, placing #32 on the daily leaderboard. Nobody asks an AI for the best brand. They ask for the best laptop, and the answer is a shelf of products. Optifeed Radar is an open-source CLI + MCP server that checks whether AI actually recommends YOUR products. Name them, and it asks real shopper questions that never mention them across ChatGPT, Gemini, Perplexity and Claude with your own API keys, then shows which products were never recommended and which rivals took the slot. Every score opens into the raw answer. Runs locally, MIT.
On the analytics side, Optifeed Radar competes within Open Source, Marketing, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Optifeed Radar performed against the three products that launched closest to it on the same day.
Who hunted Optifeed Radar?
Optifeed Radar was hunted by Erdem Gezer. 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 Optifeed Radar including community comment highlights and product details, visit the product overview.
Erdem Gezer
Hey Product Hunt! I'm Erdem, co-founder of Optifeed (the autopilot for ecommerce marketing). Optifeed Radar is our new open-source project, and it exists because buyers moved to asking AI what to buy, and merchants had no way to know if the engines ever mentioned them - short of asking one prompt at a time.
So it does what I did manually, in one command:
🔎 Generates the questions real buyers ask (none of them name your brand)
🤖 Sends them to ChatGPT, Gemini, Perplexity and Claude with YOUR API keys
📊 Scores mentions, position, sentiment, and share of voice vs competitors
🧾 Every score opens into the raw answer that produced it - no black box
🛒 Beta: name your products and see which ones AI never recommends, and which rivals it names instead
Honest notes, because you will ask: it spends your own API credit (a quick four-engine run measured about $0.41-0.46; the full default pack is roughly 2.5x that, which is what the demo video runs), scores are estimates from a sample and move between runs (there is a diff command instead of fake precision), and engine APIs are not identical to the consumer apps. It is MIT, runs locally, and it is also an MCP server, so your own AI agent can run the checks.
It is for developers, technical marketers, and agencies who want evidence instead of a dashboard subscription.
Special thanks to @nevo_david- his work and encouragement gave me a lot of inspiration and motivation to keep building and ship this.
I would love feedback on the scoring methodology (it is published in the repo) and on which engines to add next. I will be here all day.
npx optifeed-radar check yourbrandcom