An AI-powered shopping layer that lives inside Reddit, converting organic product discussions into transparent, purchase-ready recommendations — without compromising the trust that makes Reddit, Reddit.
Monetization can't come at the cost of the community. These four goals helped me hold that tension throughout the project.
Capture and convert high-intent product discussions into measurable commerce value.
Keep transparency and clarity high so monetization never undermines user confidence.
Enhance community discussions without distorting or replacing authentic voices.
Balance revenue generation with long-term ecosystem health and user retention.
I interviewed four Reddit users about how they research products, what they trust, and where they get stuck — then mapped what they told me into themes.
Understand current product research behavior on Reddit; identify trust signals; surface friction; assess receptiveness to AI summaries and transparent monetization.
Tell me about the last product you researched on Reddit. What was your process?
When do you feel ready to actually make a purchase decision?
How do you decide which comments or users are trustworthy?
What signals make a recommendation feel authentic vs. promotional?
What frustrates you most when browsing product recommendation threads?
Have you ever felt overwhelmed even after reading multiple opinions?
How would you feel if Reddit summarized the top community-recommended products?
What would make this kind of AI feature feel manipulative?
Participants use multiple platforms — Reddit, YouTube, Amazon — to triangulate before buying. Reddit is valued as a validation tool because discussions feel authentic and conversational, not like a review site.
Users rely on community signals: upvotes, depth of comments, repeated product mentions. Human-feeling, casual language is the single biggest reason they trust a Reddit recommendation.
Reddit research is rewarding but exhausting. Users read multiple threads to reach confidence, and explicitly asked for condensed summaries of key recommendations.
People are open to monetization — but only if it's transparent and clearly separated from organic recommendations. Poorly labeled sponsored content reads as manipulation and breaks trust.
Users want faster ways to extract consensus from community discussions — but transparency and authenticity are non-negotiable.
Daniel works at a cloud infrastructure company in Seattle. Outside of work he plans weekend trips for road biking, hiking, and landscape photography — and he relies on Reddit to figure out what gear is actually worth buying.
There's a user side and a business side to this — and addressing only one of them wouldn't really solve it.
Daniel relies on Reddit to research and validate upgrades for his hiking gear, trusting authentic, experience-based recommendations from real users.
But insights are fragmented across lengthy discussions, requiring significant time to compare opinions and cross-check information across platforms.
As a result, Reddit does not effectively support him in turning community knowledge into confident purchasing decisions.
Despite hosting high-intent product discussions, Reddit lacks a structured way to translate community insights into actionable shopping experiences.
Valuable user intent is not captured, limiting Reddit's ability to monetize product discovery while maintaining user trust.
The opportunity sits in the gap: helping users decide faster while opening a transparent revenue channel.
Mapping the existing flow helped me see where the design could be most useful.
Three questions that opened up the design space.
An AI-powered recommendation layer that identifies high-intent product discussions and summarizes community consensus into transparent, purchase-ready product recommendations.
No single pattern in the market does the whole job. I looked at how each one works and tried to bring together the parts that fit.
Quick to understand a discussion, but doesn't actually support a clear product decision.
Connects discovery directly to purchase, making the moment faster and smoother.
Helpful for guidance, but often feel biased or too promotional.
Make it easy to evaluate by laying out details side-by-side.
Trust climbs when you can see who's sharing the review and what their experience is.
Rather than invent a new visual language, the design stays inside Reddit's existing system — same typeface, same orange, same component logic. The work is in the layout, hierarchy, and new patterns added on top.
Before committing to a polished prototype, I built a mid-fidelity version of the core flow in Proto.io and walked one participant through a real shopping scenario — researching a new hiking backpack on Reddit. The flow held up, but four small issues surfaced that needed real fixes.
The "Ask" button sat next to the search bar and visually resembled a recording button — the participant wasn't sure whether it acted independently or was part of search.
The participant wanted clearer signals that summaries came from real users — and suggested making the quoted comments tappable so they could read the original source.
A small "…" element near the AI summary read as interactive but wasn't. A confusing affordance that needed to either do something or disappear.
The participant wanted to see star ratings and review counts on each recommended product to assess credibility faster — currently missing from the initial design.
Participant Lindsey Meisterheim was asked to upgrade her hiking backpack using Reddit, then walked through the full happy path while thinking aloud.
The session recording was lost in transfer — captured detailed notes, but next round, redundant backups go in from the start.
The final hi-fi prototype incorporates each piece of testing feedback. Below is what changed between the initial design and this version — and the live prototype follows. Tap through it to see the full flow.
The button was relabeled and moved into the AI summary surface itself, so its purpose is obvious. No more ambiguity with the search bar.
Every quoted comment opens a bottom sheet showing the original Reddit thread, the author's karma, and the replies — so users can verify the source in one tap.
Instead of removing it, the three-dot element was wired up to do what people expected — expand a longer summary view. The affordance now matches the behavior.
Every product card now carries a star rating and review count alongside the community tags — closer to the validation cues people use elsewhere.
The prototype starts on a real-feeling Reddit thread about hiking backpacks, surfaces an AI summary of the conversation, opens into a side-by-side comparison of the top community picks, leads into a product detail page with quoted reviews, and ends with a post-purchase share-back loop that feeds the next user's research.