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Most people treat a side project as a single bet. Ramsri Goutham Golla treats it as a portfolio, and the difference shows in how durable his results have been.
Ramsri is a full-time data scientist and microSaaS builder from India who runs three AI products alongside his job. Questgen.ai is an AI quiz generator that turns text, PDFs, or URLs into multiple-choice, true/false, and fill-in-the-blank questions. Supermeme.ai turns any text into complex, multi-panel memes ready for social media. And AiArtist.io is a recently launched AI motion graphics generator that creates videos from text.
The track record across them is substantial. Questgen has generated over $150k in revenue and 150,000 registered users since launching four years ago. Supermeme has crossed 2 million registered users and hit Product Hunt's number one spot on launch day. AiArtist, still new, has its first 20 paying users.
At their peak in 2025, the apps reached $100k in combined ARR. They currently hover between $6k and $7k MRR, with more than 2.5 million registered users across the portfolio.
Here is how he built and sustained all three, including a clear answer to the question every AI product builder is now asking.
A Bet Placed Two Years Early
In 2021, Ramsri went looking for ideas at the intersection of AI and education. Quiz generation stood out to him as an ideal challenge, precisely because it was not good enough yet. He believed the underlying technology would improve substantially over the next few years, and he wanted to be positioned when it did. That bet paid off: after 2023, text-to-quiz generation gained real momentum.
Before building, he talked to educators to understand their actual pain points. He learned that they outsourced quiz generation to item bank companies and ran several stages of review on the results. That surfaced a specific opportunity: help those companies manage quizzes with their own internal workforce instead of outsourcing the work entirely. That insight became Questgen.
The build itself followed a path many non-technical-first founders will recognize. He started with no-code tools like Bubble to get a working version quickly, but hit the ceiling those tools impose by abstracting away the code. So he learned a full-stack framework, Next.js, and rebuilt Questgen as a full-scale application. The current stack is Next.js with Supabase for backend and authentication, and these days he uses tools like Claude Code to vibe code and add features faster.
From Pay-Per-Use to Subscriptions
Questgen initially launched on a pay-per-use model, but the team soon converted to subscriptions. Plans start at $10 to $15 for the starter tier and go up to $50 for higher usage, with the annual plan priced 40% below monthly.
The economics are healthy: after covering LLM costs, the products run at 60 to 70% margins. That margin discipline is part of what lets a solo builder sustain three products without a team absorbing the overhead.
Open Source as a Launch Strategy
One of Ramsri's most effective moves was counterintuitive: he open-sourced Questgen as a GitHub repository first, letting anyone download the models and self-host quiz generation locally or inside their own organizations.
Open source turned out to be an excellent launch and promotion channel. People are far more likely to share an open-source project, so it gained traction through word of mouth quickly. GitHub itself drove traffic, and he paired it with a steady stream of blog posts on question generation broadly, which kickstarted his SEO.
Then came the quiet conversion step. Once Questgen was pulling meaningful monthly traffic, he added the web app URL to the top of the GitHub repository's README. That single placement let people who did not want to self-host become paying customers instead. The free, shareable version fed the paid one.
SEO and Building in Public
Since launch, SEO has been the central growth focus across the products. Ramsri's system includes alternate landing pages, deliberate keyword targeting, and programmatic SEO pages generated for individual memes on the Supermeme side. It is a compounding approach rather than a spiking one, built to keep bringing traffic long after any single piece is published.
Alongside SEO, he is a committed builder in public. From the earliest stage of an idea, he shares his progress online, primarily on X and LinkedIn, posting almost every day. His two-part conviction here is simple and worth adopting: share consistently, and do not treat a launch as a one-time event. Launching and relaunching is part of the system, not a sign that the first launch failed.
How Supermeme Hit Number One on Product Hunt
Supermeme's Product Hunt launch went viral and ranked number one for the day. Ramsri is specific about why it worked, and none of it was luck:
They borrowed an audience they did not have. Since none of the team had a large following and none had run a Product Hunt launch before, they reached out to Hunters on X until they found one genuinely interested in the product, who then launched it for them.
They aligned with where technology was heading. Text-to-text AI was saturating just as AI was entering image generation. One-click text-to-AI memes sat exactly at that intersection, matching both the technological trend and audience demand. This timing proved crucial.
They activated the audience they had built in public. On launch day they posted across every social channel, and the people who had followed the build showed up to support it. The earlier founder-led marketing paid off precisely at this moment.
They prepared everything in advance. All launch materials were ready ahead of time, and the landing page screenshots were built to convey the core message of easy AI meme generation at a glance.
Competing When the Model Makers Ship Your Feature
The hardest challenge Ramsri faced is the one now facing every AI product builder: what happens when ChatGPT, Anthropic, and the other model makers roll out native versions of your feature. When they did, his traffic and subscriptions flattened out.
His answer is the most useful takeaway in the whole story. Native integrations are strong at generation but weak at input and output formats. Educators, for instance, need quizzes exported in specific formats their learning management systems can ingest, and those formats are not readily available online for a general model to learn. A native app cannot easily replicate them.
"Native integrations lack input and output formats. Users need quizzes in specific formats for their systems. Those formats have been my differentiator."
The lesson generalizes well beyond quizzes. The defensible ground for a small AI product is rarely the raw generation, which the model makers will always do well. It is the unglamorous, domain-specific work at the edges: the formats, the integrations, the workflow details that a general-purpose tool has no reason to handle. That is where a focused builder can hold a position that a frontier lab will not bother to contest.
Launch, Iterate, Repeat
Ramsri's advice reflects exactly how he operates. Continuously experiment with new models and tools as they arrive, because that experimentation is where both ideas and challenges surface. Launch lightweight demos and create small pieces of content on platforms like X or YouTube to gauge an idea's reception before committing to it. Then double down on the ideas that clearly resonate.
It is a portfolio approach to ideas as much as to products: place many small, cheap bets, read the signal honestly, and pour your energy into the ones that work. Across three products and 2.5 million users, that method has held up through both the AI boom and the moment the big model makers arrived on his turf.
You can follow along on X and LinkedIn, or check out Questgen.ai, Supermeme.ai, and AiArtist.io.
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