Reddit marketing that gets your brand into the AI answer.

Buyers ask Reddit before they ask you. Google ranks those threads, and ChatGPT, Gemini and AI Overviews read them to decide who to recommend. We put your product into the threads that matter, in the first 48 hours, and track it all the way to the AI answer.

views on one r/Frontend post
216K173K of them in the first 2 days
views in 3 hours
18K#1 post on r/openclaw that day
Google Forums result in a week
#1for “algolia autocomplete alternatives”
post on r/ecommerce, 4 hours in
#12K views, 31% from the US

Results, by industry

Real screens from client campaigns: Reddit Post Insights, Google results and the AI answers that follow. Cropped, never edited. Pick an industry.

Search, APIs and frontend infrastructure, sold to engineers who research everything on Reddit first.

r/FrontendDay 5

How do u prevent users from submitting 1-word searches?

Reddit Post Insights for an r/Frontend post: 216K views five days after posting, the account's #7 post of all time216K views
A developer question about vague search queries. 173K views by day 2, 216K by day 5.
r/webdevDay 3

Trying to improve search UX without replacing algolia.

Reddit Post Insights for an r/webdev post: 22K views three days after posting22K views
22K views in 3 days, most of them in the first 12 hours.
Google ForumsWeek 1

algolia autocomplete alternatives

Google Forums results for 'algolia autocomplete alternatives' with the r/webdev thread first, one week after posting#1 result
The same r/webdev thread, Google's first forum result for the client's target query a week later.

The posts behind the numbers

Three of the four posts from one developer-tools campaign, word for word, with the replies that kept each thread going. The product is never in the title and never the only option in the body.

01 · The problem
r/Frontend· 6 Sep 2026

how do u prevent users from submitting 1-word searches?

im tired of building complex filter UIs just for users to ignore them, type "shirt" into the search bar, get 400 results, and instantly bounce. our backend retrieval is actually really fast, but the human query formulation is awful.

instead of writing 500 lines of custom debounced react filters to try and force them into categories, im looking at intercepting the keystrokes with an intent layer on the frontend. basically an ai-autocomplete that formats their vague text into a structured JSON payload before it ever hits the API.

has anyone used frontend intent layers to fix vague search? looking at commandbar vs just dropping in a lighter ai-autocomplete hook.

  • users typing "shirt" and leaving is the most accurate description of ecommerce frontend dev i've ever read

    • OP

      literally. you spend 3 weeks building faceted navigation and they just treat the input bar like a magic 8 ball.

02 · The cost
r/nextjs· 8 Sep 2026

vercel serverless bills spiking because of terrible user search queries

we use nextjs with a standard postgres backend. our vercel compute and database read bills are getting way out of hand because users just mash the search bar with 1 or 2 vague words, forcing the serverless functions to do massive fuzzy lookups that return basically the whole catalog.

migrating to a dedicated search engine like algolia feels like overkill (and just shifts the cost). so im thinking about fixing this entirely on the client side. if we put an ai-autocomplete intent layer in front of the text box, we can structure their messy queries into strict parameters before firing the server action.

anyone doing intent parsing on the frontend in nextjs to save serverless costs?

  • this is exactly why i stopped using serverless for anything search-related. a small VPS running meilisearch is $5 a month and doesn't care how many garbage queries it gets.

    • OP

      that's fair, we might eventually move search off vercel entirely if this frontend fix doesn't work.

03 · The build
r/node· 11 Sep 2026

how to sanitize and structure terrible search inputs before they hit express?

my node/express api is getting absolutely hammered. users type conversational nonsense into our frontend ("shoes that dont hurt my back for standing all day"), and our backend regex/fuzzy search just panics and returns 0 results.

i don't want to spin up python just to do NLP backend parsing, and passing this to an LLM on the backend adds crazy latency. i saw some devlogs about using a frontend ai-autocomplete intent layer to structure the raw text into clean JSON parameters before it even hits the express route.

has anyone offloaded query intent to the frontend? what are u using?

  • "shoes that dont hurt my back" is a wild search query. people really think every input box is chatgpt now

  • just use typesense. it handles conversational typos way better than regex and has a great node client.

How a Reddit thread ends up in an AI answer

AI engines search before they answer. For buying questions, what they find is usually a Reddit thread.

  1. 01

    A buyer asks

    “how to improve shopify search without paying a lot for plugins?” Typed into Google, ChatGPT or Reddit itself.

  2. 02

    Google ranks the thread

    For buying and comparison questions, Reddit threads sit in the top ten and in the Forums tab.

  3. 03

    AI reads what ranks

    ChatGPT, Gemini, Perplexity and AI Overviews search the web, pull the top pages and pick the clearest passages.

  4. 04

    The answer repeats it

    What real users said in the thread becomes the candid view the answer quotes, sometimes almost word for word.

  5. 05

    Your brand gets shown

    If your product is part of that conversation, it is part of the answer. If it is not, a competitor is.

Google search for 'algolia autocomplete alternatives', Forums tab: the r/webdev thread 'Trying to improve search UX without replacing algolia.' is the first result, with 30+ comments, one week after postingGoogle Forums
Google, Forums tab, “algolia autocomplete alternatives”: our r/webdev thread is the first result one week after posting, above threads from 2021 and 2022.
“how to improve shopify search”Google US, top 10, 21 Oct 2026
  1. 1AI Overviewgoogle.com
  2. 2Common problems with Shopify searchprefixbox.com
  3. 3The ultimate Shopify search engine optimization checklistamericaneagle.com
  4. 4Videosyoutube.com
  5. 5r/shopify · What are people using for better search?reddit.com
  6. 6Shopify search bar settingsexpertrec.com
  7. 7People also askgoogle.com
  8. 8r/shopify · Improving search resultsreddit.com
  9. 9Shopify search not showing relevant resultswizzy.ai
  10. 10r/seogrowth · How can I improve SEO of my Shopify store?reddit.com

3 of the top 10 are Reddit threads, more than any other site. This is the page AI engines read when a buyer asks the same question.

How a Shown Reddit campaign runs

Four posts in four subreddits over a month, built from the questions your buyers actually ask, and tracked all the way to the AI answer.

posts per campaign
4
subreddits, one per post
4
of hands-on work per launch
48h
AI engines tracked weekly
6
  1. Week 0

    Map the questions and the rooms

    We start from what your buyers type into Google and ChatGPT, not from your feature list, then pick the subreddits where those buyers already ask.

    • Search volume and today's AI answer for every query
    • Subreddits chosen by buyer, rules read first
    • The words we want AI to use about you, decided up front

    Buyer questions

    Algolia autocomplete alternativesHow to improve Shopify searchBest AI autocomplete toolsHow to add AI autocomplete to my product

    Where those buyers ask

    r/webdevr/Frontendr/ecommercer/nextjs
  2. Week 1

    Write a post that belongs, then work the first 48 hours

    Each post opens with a real problem in the community's words and treats the product as one option among several. Then we stay in the thread while Reddit decides how far to push it.

    • Question-led title, no links, no pitch
    • Every comment answered with substance
    • Replies are what AI quotes later, so they get the most care
    r/Frontend

    How do u prevent users from submitting 1-word searches?

    im tired of building complex filter UIs just for users to ignore them, type "shirt" into the search bar, get 400 results, and instantly bounce.

    users typing "shirt" and leaving is the most accurate description of ecommerce frontend dev i've ever read

    72 comments
    173K views by day 2
  3. Weeks 2 to 3

    Four posts, four angles, four rooms

    One post is a moment. Four posts in four subreddits give Google and AI four independent threads telling the same story from different sides.

    • The problem, the comparison, the cost, the build
    • Different subreddit and voice for each
    • Timed to each community's active hours
    The problemr/Frontend

    How do u prevent users from submitting 1-word searches?

    The comparisonr/webdev

    Trying to improve search UX without replacing algolia.

    The costr/nextjs

    Vercel serverless bills spiking because of terrible user search queries

    The buildr/node

    How to sanitize and structure terrible search inputs before they hit express?

    One story · four independent threads for Google and AI to find
  4. Week 4 and on

    Track it all the way to the AI answer

    Views are only the first layer. Every week we check where each thread ranks in Google and whether the AI engines mention you for the mapped questions, with screenshots.

    • Reddit reach, upvote ratio and comments
    • Google position and Forums tab for each thread
    • Mentions in ChatGPT, Gemini, Perplexity, Claude, Copilot and AI Overviews
    Reddit reachViews, ratio, comments
    Google rankTop 10 and Forums tab
    AI mentions6 engines, weekly

    Checked every week in

Prithvi JadwaniFounder, Shown

Which threads does AI read for your category?

Book 30 minutes with Prithvi. Before the call we run your buyers' questions through Google, ChatGPT, Gemini and Perplexity and show you the Reddit threads they lean on today.

Book a call with Prithvi 30 minutes · free · you keep the prompt report

02How does Reddit change what AI says about your brand?

AI answers have opinions. Ask “is X worth it” or “X vs Y” and the engine summarises what people say: who it is for, what it costs, where it falls short. That summary comes from a small set of sources, and for most B2B categories a few Reddit threads are among them.

This works in both directions. One unanswered complaint thread can become the line every engine repeats about you for a year. A thread where your product is discussed fairly, with the trade-offs explained and the right use case named, gives the engine a better sentence to use.

So Reddit marketing for AI is not only about being mentioned. It is about how you are described: the category you are placed in, the competitors you are compared with, and the problem you are known for solving. We pick those words before we write the first post, then check whether the AI answers start using them.

03What makes a Reddit post take off in the first 48 hours?

Reddit's Post Insights shows views hour by hour, and the pattern is the same every time: almost all of a post's reach arrives in its first day. Our r/openclaw post took 18K views in 3 hours, then flattened. The r/Frontend post was the exception: it kept thousands of views an hour coming for its first 12 hours and was still adding views on day 2.

What the winners share:

  • A title that is a real question. “How do u prevent users from submitting 1-word searches?” reads like a developer on a bad day, because that is the frustration the product solves.
  • A specific, relatable pain in the first two lines. Users typing “shirt”, getting 400 results and leaving. Communities upvote the problem they recognise.
  • Options, not a pitch. The post compares approaches and names more than one tool. Threads that read like ads get removed or ignored, and AI engines discount them too.
  • The right room. A question about search UX belongs in r/webdev; a question about plugin costs belongs in r/ecommerce. Same product, different post.
  • Answers in the comments. The thread keeps rising while people get real replies. Those replies are also the passages AI quotes later.

04How do you measure a Reddit campaign?

Views are the first number and the least important one. They tell you the post was seen. What a B2B team should care about is whether the thread keeps working: does it rank in Google for the target query, does it appear in the Forums tab, and do the AI engines start mentioning the brand for the questions you mapped.

A note on Reddit's own tracker: the Post Insights chart only draws the first 48 hours, so the bars stop moving after day two. The total view count above the chart keeps counting. Our r/Frontend post showed 173K views on day 2 and 216K on day 5 with the same chart.

We report all three layers every week: Reddit reach, Google position for the thread, and AI mentions across ChatGPT, Gemini, Perplexity, Claude and AI Overviews for the mapped queries.

05Why pair Reddit with AEO?

Reddit gets you into the conversation fast. Answer engine optimisation makes sure that when an engine follows the conversation back to you, your own site agrees with it: answer-first pages, comparison and alternatives pages, clear pricing and use cases, structured data, and pages that load for AI crawlers without JavaScript.

One without the other leaks. A great thread that sends AI to a vague homepage gets you a vague mention. A perfect site nobody talks about rarely gets recommended. Together they give the engine a third-party reason to mention you and a first-party page to cite. See how that played out for B2B software in our case studies.

Prithvi JadwaniFounder, Shown

Get your brand into the threads AI reads.

Book 30 minutes with Prithvi directly. Before the call we run your buyers' prompts in ChatGPT, Gemini, Perplexity and Google AI, so you see who gets shown today and how we get you in.

Book a call with Prithvi 30 minutes · free · you keep the prompt report