# Here's How Supermemory Became THE AI Memory Company In three months we drove 1M+ SEO impressions and 90% traffic growth. Below is the playbook we used to make Supermemory the default name in AI memory. --- ## Results | Metric | Value | |--------|-------| | SEO Impressions | 1M+ | | Traffic Growth in 3 Months | +90% | | Reddit Views on a Single Campaign | 200K+ | | SERP for "mem0 vs supermemory" | #1 + #2 | --- > "I've worked with them. Insane agency, craft and talent. Great at talking to customers. They wrote content that organically brings us millions of impressions on SEO." > — [Dhravya Shah, Supermemory](https://x.com/DhravyaShah/status/2033581377199968309?s=20) --- ## The Problem: Education Was The Whole Game When we started, AI memory was barely a category. People had heard the words but couldn't tell you what the product did, or why they'd reach for it instead of stuffing more context into a prompt. Supermemory was sitting at the edge of a field that didn't quite exist yet. In a category that doesn't exist yet, developer education is the whole game. Supermemory had to be **THE AI memory company**, not just a memory tool people had heard of. > **Dhravya came to us because category education isn't a one-channel project, and he didn't have the bandwidth to build the strategy and execute on it himself.** That's the wall almost every founder hits. They know education matters but can't run SEO, social, Reddit, GEO, and case studies in parallel on top of building the product. DevRels at later-stage companies have the time, but they're pulled in five directions and end up doing one channel well instead of five together. Either way, developers smell BS in two paragraphs, so everything has to be polished enough that a senior engineer would forward it. --- ## Audience: Knowing The Reader Before we wrote anything, we had to know who the reader actually was: day-to-day, tools in their stack, the frustration that would lead them to AI memory in the first place. Skip that work and everything downstream becomes guesswork. Our trick was to picture one developer and walk through their day. Not a persona document, a real person. We'd trace the exact moment they might stumble into AI memory, then work backwards through every search and rabbit hole that could have led them there. Vector databases were the obvious example: anyone building with embeddings ends up there, and the AI memory crowd is mostly an extension of that one. RAG and context windows were the same story. > **Developers are curious by nature, which is the gift. There's almost always a parallel topic you can ride into their attention.** --- ## Strategy: Mapping The Surround Once we understood the reader, we mapped what we'd write about. Seven buckets came out of it: MCPs, context windows, AI memory, vector databases, LLMs, RAG, and AI agents. None of them were ours outright, but every one sat right next to AI memory in a developer's head. The cadence and channel split mattered as much as the topics. Here's the strategy memo Naman wrote at the time, lightly edited. We've left it raw on purpose: > here's the thing with this memory industry, it's not established yet, so in terms of traditional SEO there's not a whole lot of search terms we can target. we'll go after surrounding topics like ai memory, rag, vector dbs, basically everything I listed above. > > but I think with this industry, it's still a lot of developers coming across this somewhere, and they're like *"dang this is cool I wanna try this out too."* the advantage we have is social media. so what we really should be doing with our content strategy is: > > - **12–13** posts hard SEO optimized going after low-competition search terms about all the related stuff > - **2–3** tutorials of building cool stuff using Supermemory's MCP. you can leverage social to share that this is what's possible, that gets more devs to go *"dang"* > - **1–2** case studies per month for the founders who are looking to make a business decision Every bucket fanned out into pillar pieces and a tree of related queries. The plan turned into a library — the published blog index at [supermemory.ai/blog](https://supermemory.ai/blog). --- ## Listening: Reading The Threads Alongside the topic mapping, we were just reading. Developers were already talking about AI memory across r/LangChain, r/LocalLLaMA, and a long tail of smaller subreddits, and the threads told us more about what they cared about than any keyword tool could. We mapped every thread and reply that mentioned the surround. Each recurring question became a content brief. The recurring questions kept showing up in the same shape: how do I manage memory in agents? Which vector DB for conversational apps? Mem0 versus Supermemory? Cross-context versus single-thread? Each thread was a content brief written for free by our target reader. We just had to listen. --- ## Topics: Long Tail, By Hand Once we had the threads mapped, we passed them through Claude to cluster by content bucket. That gave us a clean view of which question categories were getting traction. From there we moved into Ahrefs to find keywords we could actually rank for. Quick aside on what **long-tail keywords** actually are. They're the long, specific queries developers type when they're stuck. Lower search volume than head terms but much higher intent, because the person searching has already worked out exactly what they need: | | Head Term | Long-Tail | |---|---|---| | Example | "ai memory" | "how to add long term memory to an llm with postgres" | | Reality | Everyone fights for it. Can't win. | Specific, low competition. Ours to win. | The Ahrefs work wasn't fancy. We filtered for high volume, low competition, opened the SERP for each candidate, and decided if we could win the first page. The judgment did most of the work, not the tool. We'd also pull competitors to see which topics they ranked for and what angles worked. > **A lot of this was manual judgement.** On top of that, we leaned into what developers actually like. They trust data over opinion, which is why benchmarks sat at the top of our list every month. Anything with real numbers on the page outperformed the same idea delivered as a listicle, easily. --- ## Writing: One Unique Thing By then we had the audience, the topics, and the keywords. The remaining question was how to actually write each post in a way that earned attention. One rule ran underneath everything: > **What's one unique thing we can do here that nobody else would take the effort to?** Take **"best open-source embedding models."** It was getting asked over and over in the subreddits where our audience hung out, so we knew the demand was real. But when we looked at what was already ranking, nearly all of it was thin listicles repeating the same five model names. Only one was running benchmarks, and even that didn't go deep. | | What Others Did | What We Did | |---|---|---| | Approach | Boring ass listicles | Proper benchmark + methodology | | Result | "Top 10 embedding models in 2024." | Real numbers. Reproducible. Devs ate it up. | So we built a proper benchmark. Picked the models, wrote a clear methodology, ran the tests, published the numbers with a writeup explaining how to read them. It took significantly longer than another listicle would have. It also shipped as something developers could actually rely on, and it did super super well. --- ## Channels: A Goal Per Post A monthly strategy isn't enough on its own. Every individual post needs its own job. Is it supposed to rank on Google? Is it built for Reddit? Are we writing it because we want it cited inside a Perplexity answer? That choice changes everything downstream: the headline, the length, the structure, where the post lives after publish. > **Your primary goal determines your approach to that blog post significantly.** Our default mix was Reddit and GEO. Answer engines were pulling a lot of their context from Reddit, which meant one well-built campaign earned distribution twice: through the original subreddit audience, and again through the engines scraping it. **Eight posts a month** isn't high volume on paper, but the effort per post made the cadence feel right. --- ## Templates: Set Recipes We had set templates for the major post types: cookbooks, tutorials, comparisons, listicles. Each had its own checklist so we weren't reinventing structure every time. The cookbook one was the most opinionated because the format breaks easily, so here it is in full: | # | Requirement | Note | |---|---|---| | 01 | A clear outcome | What can the reader actually do at the end? | | 02 | Step-by-step code, explained | Don't dump code. Explain every snippet as you write it. | | 03 | A video | For social repurposing. | | 04 | A working demo | Something readers can actually click into. | | 05 | A "cool" factor | If it's not cool, it's not worth writing. | > **If a cookbook doesn't have a 'cool' factor, don't bother writing it.** Every post got an SEO pass at the end, but the structure came first. Format discipline is easier than rewriting a post in the wrong shape. --- ## Case Studies: Three Channels Per Post Case studies were our highest-leverage post type because a single one did three jobs at once: bottom-of-the-funnel content for sales, ranking on Google for high-intent comparison keywords, and repurposable into a Reddit post. The trick was making sure each one delivered on all three. The biggest one was Mem0 versus Supermemory. Mem0 was Supermemory's largest competitor, which made **"mem0 vs supermemory"** about as high-intent a keyword as it gets. We dug through Supermemory's customer list, found someone who'd switched from Mem0, interviewed them, and built the post around that real switching story instead of an abstract feature comparison. Interviews stayed short. We'd pull from *The Mom Test* because the questions don't leave room for the polite-but-useless answers people default to: | | The Mom Test, abbreviated | |---|---| | Q1 | What was the problem you were facing? Walk me through it. | | Q2 | What solutions did you try out? | | Q3 | Why us? | | Q4 | Was there a specific wow moment? | Four questions, enough raw material. From there we'd weave the answers into a story built around a high-intent GEO angle. The Scira AI case study, [for example](https://supermemory.ai/blog/why-scira-ai-switched), ranks #1 for "mem0 vs supermemory" today. Every time someone Googles that comparison while evaluating, our case study is the first thing they see. > **Case studies need impact numbers. That's what makes them land.** We're now running this same playbook for other developer-tools companies. --- ## Reddit: Quality Over Quantity Ranking the case study on Google was straightforward. Reddit was the harder one. Subreddits don't tolerate self-promotion, and a plain customer-story link would have been downvoted before anyone read it. We needed to deliver something on top of the case study that the community actually wanted on its own. > **Reddit doesn't reward a plain case study. You have to deliver value on top of it.** So we ran a benchmark. Real numbers, head-to-head, on the metrics engineers building memory layers actually care about. We posted it as ["mem0 vs supermemory: numbers on what's better"](https://www.reddit.com/r/LangChain/comments/1o080oz/mem0_vs_supermemory_numbers_on_whats_better_for/), with the case study cited as the receipt at the bottom. The post itself was framed around the numbers, which is what made it land. The results stacked up: | Channel | Result | Detail | |---------|--------|--------| | Reddit | 200K+ | Views across the campaign. | | Google / Blog | #1 | "mem0 vs supermemory." | | Google / Reddit | #2 | Same keyword. We own positions 1 and 2. | Net effect: we own both top results on the highest-intent query in the category, and that traffic converts directly into sales calls. ### Our Broader Reddit Playbook The Mem0 campaign wasn't a one-off. The same pattern ran underneath everything we posted, and the principle was simple: > **Quality > quantity. Always.** - **Analyze each subreddit** your audience actually hangs out in. - **Study the top-performing posts.** What patterns do they follow? - **Cross-reference your blog plan** and repurpose accordingly. If a topic doesn't have a Reddit thread yet, that's a good signal. - **1 good campaign per week** is more than enough. - For devs, **numbers and cool demos win**. Being genuinely helpful in the comments doesn't hurt either. One real campaign a week, built with that level of care, beats five mediocre ones every time. The audience can tell. So can the engines pulling from those threads later. --- ## Reception: The Goodwill Came Back When you do this work at the level of care developers can feel, the audience tends to talk back. Founders, engineers, and builders started posting about Supermemory's content unprompted on Twitter, and the secondary distribution from those mentions ended up as valuable as the traffic from Google and Reddit. A few of the replies: - "These posts are the reason we keep choosing you guys over the competition." - "Absolutely golden tips, pieced beautifully together." — Aryan Keluskar, on the LLM costs blog - "You earn the real respect with these community efforts." — Shreyansh - "supermemory >>>>>>" — Zaid, quoting the Scira AI switch announcement --- ## Thesis: GEO Is Overcomplicated We've seen this pattern across companies now. The conversation around GEO has gotten massively overcomplicated: new tools every week, AI content at scale, automation stacked on automation, all promising that ranking is now a procurement decision instead of a craft. > **You don't need the next fanciest AI tool to rank.** You need someone willing to sit down and understand what your product does, who you're building it for, how that person thinks, and execute against that understanding consistently. Most of the work is judgment, and the leverage comes from getting it right early. > **AI won't understand your ICP as well as you do. AI won't execute good content for you. You need people.** AI can speed up parts of the work, and we use it every day. But without oversight on the things that matter (positioning, voice, angle, what's even worth writing about), it produces output that ranks technically and converts nobody. That's the philosophy we take into every engagement. --- ## We're Taking On A Few Series A Companies For Blogs And DevRel Same philosophy on every engagement. Humans do the thinking, AI accelerates the work, and the result ranks AND converts. [Book a Conversation](https://savvycal.com/manicule/intro) | [View The Live Blog](https://supermemory.ai/blog) --- © 2026 Manicule, Inc.