Hitting $30k MRR with an AI marketing product

Good luck to the founder! I am gradually building my simple software directory and took 30 minutes to post your product. am not allowed to post links yet.
venkatsoftware . com / leadmore-ai

you should have a press or brand asset page so that it would have been easier for me to download the logo. It took time for me to manually do it.

Venkat

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Solid insights here. I like how you focused on one channel instead of spreading effort everywhere. Do you think this approach still works at a slightly larger scale?

I agree with you

As an indie developer, marketing is always something I struggle with. Thanks for sharing these insights and reminding me what really matters.

This is solid advice, especially the first point.
I think most indie devs intellectually agree with “talk to users first,” but emotionally default to building because it feels like progress.

I’m curious how you personally validated “real demand” before committing — was it willingness to pay, repeated pain signals, or something else?
Also interesting that you emphasize ops + growth as a core skill now. Do you think that’s because distribution is harder, or because tools have lowered the bar on building?

Appreciate the pragmatic framing here — less romance, more reality.

I agree with you. Many founders believe strong topline revenue growth is the primary lever for a Series A fundraise. While traction is crucial, sophisticated investors dig deeper.
Their core question isn't just if you can grow, but how efficiently you can scale. They are evaluating your unit economics: the cost to acquire a customer (CAC) relative to their lifetime value (LTV). If your blended CAC is rising as you scale, it signals a potentially unsustainable model, regardless of impressive top-line figures.
A compelling Series A model must therefore move beyond simple revenue extrapolation. It should segment and de-average this data to demonstrate improving efficiency, proving that your growth builds a fundamentally sound business, not just a larger top line.

The "50-100 customer conversations before writing code" part is where most of us fail. It's uncomfortable and feels unproductive compared to the dopamine hit of shipping features. But every founder I know who actually did the research phase properly built something people wanted on the first try. The ones who skipped it (myself included on early projects) built faster but pivoted more.

Interesting that he's doing Reddit marketing automation and grew through... Reddit content marketing. That's not accidental. Your distribution channel being your product's use case is a cheat code for credibility.

One thing I'd push back on slightly: the "could have launched with one feature" hindsight is easy to say at $30k MRR. In the moment, it's genuinely hard to know which feature is the one that matters until users tell you. The real skill is launching fast enough that you learn which features to cut before you've overinvested.

Curious what the retention numbers actually look like for a credit-based model like this. Credits with refund options feel customer-friendly but could mask churn if people just stop buying rather than formally canceling.

Your journey and process are incredibly insightful for any indie dev. Focusing on user research first and staying lean in MVP development are lessons I'll definitely take to heart. Also, your approach to growth through content and relationship building is spot on for long-term success. Looking forward to seeing more from Leadmore AI and ModelFox!

i feel firstly focusing on just one platform is very niche specific good but making as whole system for all platform and integration as marketing is a real and a big pain point for more then 90% founders

thanksit's very useful

fantastic u made a really great product which is really solving a big product. well done!

very usefull!

Solid breakdown — appreciate how much emphasis you put on distribution and iteration rather than “AI magic.”

One thing that stood out is how quickly clarity around who it’s for seems to have unlocked growth. Curious in hindsight: was there a specific moment or signal where you knew you’d nailed the right customer + use case, or did it only become obvious once MRR started compounding?

How did you market the product when you had zero customers?

How long did it take you to get your first paying user?

KnowHowCentral . Com is a business and technology insights platform dedicated to empowering readers with expert analysis, practical tips, and in-depth resources on the latest trends in business, tech innovation, digital transformation, and professional growth.

This is a masterclass in focus.
Validating before building, obsessing over retention, and fighting the urge to overbuild are lessons most founders learn too late.
Doubling down on a niche instead of chasing shiny ideas is how real MRR compounds. Respect the clarity and discipline here 👏

The credit system with anytime refunds is an interesting move - lowers friction but feels risky from a cash flow perspective.

Have you seen abuse, or does the goodwill it builds outweigh the refund rate? Curious if this is something you'd recommend for other SaaS founders or if it only works for your specific use case.

AI gets a lot of attention, but what really matters is how it shapes the user’s internal experience. Most products underestimate the power of silent interaction and reflection.

Hi,

I built a small tool because I was tired of spam hitting my personal inbox 😅

EmailShield[.]app lets you generate disposable email aliases so you can sign up for websites without exposing your real email.

I’m looking for early users to try it and tell me what sucks / what’s missing.

Happy to give free access to anyone willing to share feedback.

I built an AI that replaces human sales chat on my website.

It actually talks like a salesperson, not a chatbot.

Looking for 5–10 store owners to test it for free.

Any feedback is appreciated.

The "validate via content before code" part hits hard. I did it backwards — built first, now figuring out distribution.

Currently at the "40K reddit views, 0 sales" stage with my product lol. Your point about retention > acquisition makes me realize im optimizing for the wrong thing. Cant measure retention when you dont have users yet.

Curious about one thing tho: when you say "talk to 50-100 users before building" — how did you find them without a product to show? Cold DMs? Reddit threads? Seems like the hardest part is getting people to talk when you have nothing concrete yet

This was a really practical breakdown — especially “validate via ops + content before code” and “subtraction > addition.” That advice is timeless, but it hits even harder in AI where it’s so easy to ship too many half-features.

One thing I’m curious about from an engineering angle: when you were still early, what ended up being the biggest retention driver you didn’t expect?
Was it (1) better onboarding / faster time-to-value, (2) improving output quality, (3) workflow reliability (fewer edge-case failures), or (4) something like “users trust the product because refunds/credits feel fair”?

Also, for your credit-based model — did you learn anything about the “right” unit for credits?
I’ve seen cases where the wrong credit unit accidentally pushes users toward inefficient behavior (e.g., spamming small actions). Curious if you had to iterate on what counts as a “credit-worthy” action to align usage with real value.

Thanks for sharing the full process + stack. Super helpful.

For an AI marketing product to hit $30k MRR, you need to understand your audience, deliver clear value, optimize onboarding, leverage AI for automation, and continuously iterate your strategies.

This is a fantastic write-up. The point about 'fighting the instinct to expand' really hit home for me. I'm currently building a React Native app for household management, and as a solo founder, I constantly think "OH, this would be a cool feature!' Then I'm chasing that instead of refining what I have.

Quick question on your validation phase: You mentioned talking to 50-100 users before building. Did you structure these as formal interviews, or was it more casual chatting in DMs? Finding that many people willing to talk without a product to show seems like the hardest part.

Reduce MVP from three features to one — this is the hardest lesson. The urge to add more always feels like progress, but it's usually just delay. Thanks for sharing.

This is a great example of how focus and fast iteration beat “big vision” early on.
The social listening angle is especially strong because it turns real pain into clear features.

Congrats on 30k MRR. What I’m most curious about is what actually moved the needle once you got past the initial “AI novelty” phase. A lot of AI marketing products spike early, but retention and repeat usage are where it gets real.

Did you find that your growth came more from narrowing down to a specific niche/persona, or from improving onboarding so users see value in the first session? Also interested in what your biggest distribution channel ended up being (content, partnerships, outbound, integrations) and whether it changed over time.

The most useful part of posts like this is the unglamorous stuff: what didn’t work, and what you’d stop doing sooner if you were starting again.

Really interesting read. The focus on doubling down on one niche instead of chasing multiple ideas stands out.
At what point did you feel confident enough to ignore other opportunities and commit fully to this one?

The "fight the instinct to expand" advice is gold. I just launched an AI security testing platform last week and had to force myself to cut features to ship faster.

Your point about validating via content before code really resonates. I spent weeks talking to security teams about their LLM testing pain points before writing any code. Discovered that prompt injection and jailbreak testing were the top concerns - shaped the entire product direction.

The retention > acquisition mindset is something I'm trying to internalize too. Easy to obsess over new signups when you should be asking "why aren't users coming back?"

Curious about your serverless stack choice - did you hit any latency issues with Go + Function Compute for real-time AI workflows? We went with FastAPI but considering Go for performance-critical paths.

The AI space is so crowded right now. What do you think helped you stand out and get traction early on?

What's your approach to handling API costs as you scale? That seems like a common challenge with AI products.

This really resonates, especially the part about validating demand before writing code.

I made the mistake of building first and realizing later that distribution is the real challenge. The “content + direct conversations” approach you mentioned feels like the only sustainable way to grow now.

Curious — when you were validating early ideas, what signals did you trust most to decide “yes, this is worth building”?

Huge congrats on $30k MRR! Love the discipline: validate with content first, ship one-feature MVPs, and fight the urge to overbuild. Retention > acquisition, serverless stack, and credit-based pricing, all smart, grounded choices. Inspiring blueprint for indie AI builders!

perfect <3

This aligns with what I have observed too. ~

Most people seem to think “AI product” means the tech does the heavy lifting. Ideally, it is combinations of distribution and positioning that carry it. AI simply speeds up or reduces the cost of things.

The credit-based model serves as a good illustration. Not due to cleverness, but because it enforces clarity. Users are able to sense the use. This tends to tighten feedback loops more than flat pricing.

I have observed that products are most efficient when the artificial intelligence (AI) is designd for a limited scope. One task. One effect. The instant it attempts to become a general assistant, confusion arises with respect to value, onboarding, pricing, etc.

I’m curious what informed your decision of where to draw that line early on.

Did you cut features fairly aggressively, or did users push you toward a tighter use case over time?

I’m also interested in how you decided when not to automate.

Did you purposely leave any parts manual in the beginning to keep things close to the issue?

Strong points. I've found that "don’t build before research" works best when it doesn’t mean "don’t make anything concrete". Even very rough implementations can act as research tools. Not to ship, but to surface misunderstandings early.

With AI lowering the cost of prototyping so much, concreteness early on often leads to better user conversations and faster internal alignment.

A compelling growth story shows how customer focus, fast iteration, and practical artificial intelligence can be used to steadily scale recurring revenue sustainably forward.

Really liked the emphasis on validating demand before building — especially using content and operations first.

That mindset alone probably saves months of wasted dev time.

Also interesting to see retention prioritized over acquisition so early. Feels like a very grounded way to scale AI products.

This is great

Hey Richard, love this raw breakdown, especially the "fight the instinct to expand" part. That one hits hard as a solo founder who's guilty of always wanting to add "just one more feature. "Your process (validate with content + ops first, then build minimal) is exactly how I'm trying to ship my own stuff now.

The credit-based model also feels super user-friendly; smart move. Thanks for sharing the real playbook. Rooting for Leadmore to hit $50k+ MRR soon

I'm currently in Phase 1 of my startup journey, and I have an idea that I'm testing and validating through platforms like Reddit and Indie Hackers. The best advice I've received so far is the importance of validating your concept before diving into building the core product. This step is invaluable because it not only saves you time but also helps you understand what customers actually want. Without this early feedback, you could easily end up investing time and resources into building a product no one is interested in. The process of validation has given me valuable insights and helped me shape my idea into something much more relevant and market-ready

Where do you find people to interview while validating an idea? Can you find validation/invalidation also by perusing relevant subreddits and communicating with users? Great article.

The advice about spending 1-3 months on user research before building anything is counterintuitive but so true. Most of us (myself included) default to "let me just build it and see."

What really caught my attention is the serverless architecture choice - Next.js + Go + MongoDB. That combo gives you incredible flexibility to iterate fast without infrastructure headaches.

The retention-first mindset is spot on too. It's tempting to obsess over acquisition, but if people aren't sticking around, you're just filling a leaky bucket.

Question for anyone building in AI marketing: how are you thinking about the shift from traditional SEO to AI-driven discovery (GEO)? Feels like a massive opportunity that's still underexplored.

This is a great breakdown of how Richard is building real products that actually make money.

What stood out most to me is how much focus he puts on talking to users before building. Validating ideas, testing demand, and keeping the MVP simple feels like advice many founders skip but it clearly works here.

Also, the reminder to fight the urge to add too many features is powerful. Speed, focus, and retention matter more than doing everything at once.

Congrats to Richard on growing Leadmore AI past $30k MRR, and best of luck with the new GEO product. This is very inspiring for indie builders who want to move fast and build smart.

By validating ideas first, building quick MVPs, staying focused, and using content-led growth and user retention strategies, Leadmore AI achieved $30K monthly recurring revenue.

The $30k MRR achieved with an AI marketing product demonstrates the potential of combining innovative technology with smart marketing strategies. A successful AI entrepreneur must focus, be consistent, and understand the needs of their customers.

True

Neat, thank you for your fabulous insight

An excellent insight that focuses on validating real user needs before building, shipping minimal features fast, and doubling down on content-led growth and retention clearly drives traction toward sustainable $30k MRR.

Incredible focus and discipline, Richard. Your 'fight the instinct to expand' advice is absolutely critical, especially for a solo founder. I lived this exact tension with my product, AbuByte POS—an offline-first system for emerging markets. The discipline to stick to solving one hard problem (offline sales continuity) allowed me to ship, validate with a live client, and reach a clean inflection point.

This focus is precisely why I'm now at a strategic juncture: to 100% commit to my next venture in AI, I'm passing on this complete, revenue-generating asset via a 7-day acquisition window. It's the ultimate exercise in subtraction you describe—letting go of one validated project to create the space and capital for the next big iteration.

For any founder reading this who understands the value of a battle-tested, niche system, this is a rare chance to acquire 2,300 hours of focused development at a steep discount. Your philosophy of 'ship fast, own a niche' is exactly what a new owner can execute from day one.

Love this breakdown especially the “validate with ops + content first, then build minimal and iterate fast” mindset. The credit-based pricing with refundable unused credits is also a smart way to reduce friction and align value with usage. And congrats on scaling Leadmore AI—solving Reddit distribution end-to-end (discover subreddits + track leads + safe publishing) is a real, painful problem for B2B founders.

Awesome journey and very practical insights — validating demand, minimal MVP, and fighting feature creep are timeless lessons.
Excited to see what ModelFox will look like when it launches 👀

This is a solid journey. Hitting real revenue with something people actually rely on feels very different than just chasing metrics.

I’ve noticed that growth gets a lot clearer once the focus stays on the actual problem being solved instead of piling on features or traffic tactics.

Curious how you decided which feedback to act on and which to ignore as things started to scale.

Hitting $30k MRR with an AI marketing product requires a clear niche, strong product-market fit, and consistent customer value. Focus on solving a specific marketing pain point, offer measurable ROI, and use clear pricing tiers. Leverage content marketing, demos, and case studies to build trust. Continuous product improvement, customer feedback, and scalable onboarding help drive steady growth and recurring revenue.

My friend and I have already built two apps this year.
Supaback — a simple tool to back up Supabase databases to Google Drive
Doran Pay — an app that lets you generate invoices and get paid directly through them

The problem is with marketing. We are good at solving problems or trying to be. No luck with marketing. Not a single clue with marketing. We made about 27$ just based on seo.

I build Product hunt alternative and it's the fastest among most of the product launch platforms. I made it a month ago and now got 3 sales. It's Solo Launches

I want to build a product but not able to decide what I should build. Any suggestions?

Hello, happy to help here.

Can you dm me for more information? Let’s connect and I would try my best to resolve your issue

Interesting to see AI being treated as the core product, not just a feature.

In AI products I’ve been structuring, the biggest challenge has been balancing inference cost, latency, and output quality.

Did you experiment with different pipeline approaches before scaling, or did it stabilize early on?

Really appreciate the focus on user validation before coding. In our e-commerce support business, we've found that talking directly to store owners about their pain points has been invaluable for shaping our service offerings.

I came for the AI comments.

This was a solid read and it’s clear this isn’t theory, but lessons earned by actually shipping and iterating. The focus on starting with real user conversations, keeping the MVP lean, and learning from usage instead of hypotheticals really hits home. In fast-moving spaces like AI marketing, moving early and adjusting beats trying to design the “perfect” product upfront.

A few takeaways that stood out to me:

• Doing validation through ops and content before committing to code is massively underrated and scales better than most people expect.

• The credit-based pricing with refunds feels like a smart way to lower commitment anxiety early on.

• The idea that restraint matters more than adding features when you’re a small team is something almost everyone ignores until it’s too late.

• Using Reddit as intent-driven, evergreen discovery rather than one-off promotion seems especially well suited to this market.

With AI snippets becoming a major concern for businesses, it's really transforming the importance of Reddit in general for businesses. Both LinkedIn and Reddit are now key platorms to play on for EVERY business. Will defintely give the free trial a go.

really good read, esp the “validate w content before code” part

quick q: what content format got you the first paying users

also do ppl actually use the credit refunds or is it mostly just trust?

Very helpful usecase !! have tried to post on reddit multiple times in many diff sub reddits have failed all time.

I love your emphasis on validating demand first, shipping minimal MVPs, and fighting feature creep—it's exactly the mindset that separates sustainable successes from burnout projects. Excited to see what ModelFox brings to the GEO space, will keep an eye on it!

I love credit-based pricing. I'm wondering if the refunds actually get used much, or is it just one of those policies that work just because they exist.

This is a masterclass in focus.
Validate before code, obsess over retention, and double down on one niche instead of chasing features — that’s how real MRR compounds.
Also love the reminder that ops + distribution matter as much as building, especially for indie founders today.

This is a refreshingly honest and practical take on indie building. Validating through operations and content before writing code, prioritising retention over growth, and having the discipline to stay minimal are all signals of strong fundamentals.

Love this approach, validating with users and content first is key. Reddit is such a powerful channel when you track pain and intent, not just keywords. I help B2B founders set up this kind of Reddit-driven lead generation safely, without spam or bans.

Leadmore looks like a great product. I signed up to explore. Was that built using AI tools ?

Had to learn this the hard way. The retention-first mindset especially matters when you’re small and moving fast.

Really interesting journey. I liked how you focused on solving one clear pain point instead of adding too many features early. Curious—at what stage did you start seeing consistent inbound demand versus outbound efforts?

Hi everyone, I’m Richard, the developer of Leadmore AI. Feel free to ask any questions, and I hope Leadmore AI can genuinely help you with Reddit marketing.

Recently, I’ve also been building a new product using an MVP-style approach. It hasn’t launched yet, but it has already generated $8,000 in revenue. In about two weeks, I’ll come back to share the detailed operations and growth experience.

Thanks!

What are your top 2 questions, you ask the users your researching?

Solid write-up, this reads like someone who’s actually been through the loop, not just theorizing.

A few things that really resonate with Indie Hackers folks:

Validating via ops + content before code is underrated and scales way better than “build then pray.”

Credit-based model + refunds is a smart way to reduce friction early.

The reminder about subtraction > addition when you’re a tiny team is gold. Almost everyone learns that the hard way.

Curious question: for Leadmore AI at ~$30k MRR, what ended up being the one retention driver you didn’t expect early on?

Appreciate you sharing the full stack + process — this is the kind of breakdown that’s actually useful here.

Great breakdown. The point about retention being more important than acquisition really hits home.

One thing I've been thinking about with Reddit marketing tools though - most cloud-based solutions eventually run into IP blocking issues. Reddit gets pretty aggressive with server IPs.

Curious how Leadmore handles that? I've been using a desktop-based approach for my own Reddit research (search "reddit wappkit" on google) specifically because it runs from my home IP. Never had blocking issues since switching.

Not saying one approach is better than the other - just different tradeoffs I guess. Would love to hear how you're solving the server IP problem at scale.

Really strong breakdown , especially validating ideas through real conversations before writing code. That mindset shows in the traction.

One thing that stood out is how naturally Reddit fits your approach. When Reddit content is treated as evergreen intent capture (ranking threads + value-first replies), it becomes a compounding channel, not just content marketing. We’ve seen it work especially well for AI marketing tools where users are already researching solutions in public.

Curious if you’re thinking about systematizing Reddit as a long-term acquisition asset as you scale beyond $30k MRR?

Curious about the Reddit strategy, trying hard to get into it, but Karma is tough man!

$30k MRR with a focused credit model is no joke. The part that stood out to me was validating via content before writing code. Way too many founders skip that and pay for it later.

30k is solid, what's the best way to reinvest in your product, to keep improving?

Great read. The emphasis on talking to real users early and keeping the MVP tight really resonated. I’ve seen the same thing — shipping quickly and learning from actual usage beats over-planning every time, especially in a crowded space like AI marketing. How do you decide which features are worth adding without hurting retention?

Very cool stuff

Really appreciate this breakdown — especially the emphasis on subtraction over expansion. That part resonated a lot.

One thing I’ve been struggling with as a solo founder in AI is the tension between:

• shipping something extremely minimal to get feedback, and

• feeling like the product needs a certain level of completeness to actually be taken seriously by users.

In theory, I fully agree with “one feature is enough.” In practice, I’ve noticed that in AI products, users sometimes bounce quickly if the experience feels too fragmented or ends abruptly — even if the core idea is solid.

Curious how you’ve navigated that in your own products:

• How incomplete were your earliest versions when you first put them in front of users?

• Did you ever worry that showing something too early would distort feedback (people reacting to roughness rather than value)?

The point about validating via operations and content before code also hit home. It feels like one of those things everyone agrees with intellectually, but it’s surprisingly hard emotionally when you’re excited to build.

Thanks for sharing such a concrete process — posts like this are genuinely helpful for people in the messy middle of building.

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