What are you building? Drop your AI product below
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Doing one of these because I get more from reading what others are building than from any newsletter.
I'll start.
Building AI Hive, an enterprise AI agent platform that helps mid-market and enterprise teams get from AI pilot to production in weeks instead of quarters. The hard problem we keep solving: compliance, model flexibility, and the lack of in-house AI talent on the customer side.
Product Hunt page: https://www.producthunt.com/products/ai-hive
Your turn. Drop:
- What you're building
- Who it's for
- The hardest part you're solving right now
Will read everything and upvote what resonates.
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I'm building Margn, a real-time AI-powered profit dashboard for Shopify stores.
It connects Shopify with Meta Ads, Google Ads, and TikTok Ads to automatically calculate true profit after product costs, fees, shipping, and ad spend—all in one clean interface.
It’s built for Shopify founders and DTC brands who know their revenue, but don’t actually know what they’re earning.
The hardest part right now is normalizing messy ecommerce + ad platform data in real time and turning it into a single reliable profit number that founders can trust without spreadsheets or manual reconciliation.
Drop your email to test it risk-free on your store today.
@samuelcrotti Real pain point, props for tackling it head on.
Ran a small Shopify store for a bit and the ad spend vs actual profit math was always a mess of half-updated spreadsheets, never trusted the number by the time I looked at it. If Margn can pull that into one reliable figure without me reconciling three platforms by hand, that's worth a lot more than another analytics dashboard nobody opens. The "normalize messy data in real time" part is the hard 20% though, curious how you're handling refunds and returns messing with the profit calc since that's usually where these tools fall apart.
@nolan_vu Thanks! That pain point is exactly what pushed me to build Margn.
You're also right that refunds and returns are one of the hardest parts. Our approach is to continuously sync Shopify order events and treat refunds as changes to profitability rather than static transactions, so the profit figure updates as the underlying data changes. The goal is that if something happens today, a refund, a return, or an order edit, you shouldn't have to wait for a report or manually reconcile anything to understand its impact.
There's still a lot to refine, but accuracy and trust are the priority. If founders don't believe the profit number, the rest of the dashboard doesn't matter. Really appreciate you bringing this up!
@samuelcrotti you're welcome then, hope that you and your team can manage to fix and alter the product in a positive way to deliver quality and satisfied experience to your users
@tony_kim8 This is such a clean idea
The privacy-first spot thing is the part that gets me. Every fishing buddy I know guards their spots like state secrets, so building that into the product from day one instead of bolting it on later is the right call. Honestly think the "game" layer is what'll make people actually log catches consistently instead of letting it die after week two like most tracking apps do. Gonna check out the beta, good luck with it.
SEOKRATES the SEO toolkit in 13 languages, like semrush but for normal people for normal price..
for all ecommerce owners from small to big. marketers, bloggers.
Find users... Also wanted to have great todays launch here but everyone just upvoting same stuff everyday / its sad for me because i really build that because i hate how much are costs for same platforms...
@hustlerv Really feel this comment, thanks for being honest about it instead of just posting another polished launch update. Building an SEO toolkit in 13 languages at a normal price while everyone else charges Semrush money is genuinely a good mission, that frustration is valid.
Discovery is brutal right now, the feed does favor whoever posts loudest that day, not necessarily the best product. One thing that's helped others I've talked to is going deep into ecommerce and blogger communities directly instead of relying on PH traffic alone, since that's your actual audience.
What I'm building:
reFrame. Most people can't see their own communication patterns until the damage is already done. Contempt, defensiveness, stonewalling, gaslighting. reFrame catches them in real time, both in the message you're about to send and the one you just received, and shows you the pattern before you hit send. There is also built in two way detection to read what someone has sent you. You know, the message you screenshotted and sent to 5 friends and said, "am I the crazy one here?" Someone being gaslit finally gets proof it isn't in their head. Privacy is real so messages are never stored.
Who it's for:
Anyone who's ever sent a text they wanted back. Right now we're going in through divorce and co-parenting, where one bad message ends up in a custody file and the kid is the one stuck in the middle.
Hardest part right now:
Distribution, and it's hard for the same reason the problem is hard to see. Nobody sends themselves an invoice for a bad conversation. You get charged a little at a time, one clipped reply, one eye-roll, one thing you can't take back, and none of it feels like anything until it compounds. Then the bill comes due all at once...the blow up, the tit-for-tat statements, and you're wondering how you got here. The people who need this most are the ones still being charged in small amounts who don't feel it yet. Reaching them before the blowup instead of after is the whole mountain.
Launched on Product Hunt today if you want to see it. No signup, no cost: reFrame: The X-ray of any relationship, situation, or conversation | Product Hunt
@wereframe Thanks for putting this out there, honestly this hits differently than most launches. The two-way detection piece, being able to check a message you received and not just one you're about to send, is the part that feels genuinely useful and not just a gimmick.
The line about nobody sending themselves an invoice for a bad conversation is painfully accurate. That slow compounding is exactly why people can't see the pattern until it's already a mess, especially in custody situations where every message can end up as evidence. Real problem, real product.
If you're open to checking out something else in the AI space, I'm building AI Hive, an automation platform for teams. Would appreciate any feedback or review if you have the time:
What you're building: Naxely — turns a CSV or Google Sheet into a branded PDF report with AI insights, anomaly detection, and charts in under a minute.
Who it's for: Freelancers and small agencies who send the same kind of client report every week and are tired of formatting it by hand.
Hardest part right now: Getting AI-generated summaries to reliably follow a consistent structure (lead finding → context → business implication → one action) instead of reading like a stats dump. Turns out prompting alone isn't enough — ended up moving to a delimiter-based parsing approach so the structure is enforced in code, not just requested in the prompt.
Product Hunt page: producthunt.com/products/naxely
@deepanshu_garg9 Appreciate you being upfront about the hard part, that's the most useful kind of update honestly. "Prompting alone isn't enough" is something more people building with AI need to hear out loud.
Moving to delimiter-based parsing to force structure in code instead of just asking nicely in the prompt is the right call. I've hit the same wall building automation flows, the model will follow instructions 90% of the time and that last 10% is exactly where trust breaks down for clients. Good instinct catching it early. Already upvote and wish your product launching the best
Would love your feedback on AI Hive if you have a minute, we're tackling a similar reliability problem for enterprise agents. A review would help a ton too: https://www.producthunt.com/products/ai-hive
We had 600 clients before we built the app.
My wife runs a trichology clinic in a premium hair salon. Eighteen months of tracking real people's hair and scalp conditions on a medical-grade trichoscope before we wrote a line of consumer code. The thing she kept seeing was blood results the GP had marked normal while the scalp imaging told a different story. No tool existed to connect them.
That gap became Órga Hair. Android is live now.
@gerard_brandon This is such a strong founding story, thank you for sharing it. 18 months of real tracking before writing a single line of code is rare, most people rush straight to building.
The part about blood results being marked normal while the scalp imaging told a different story really stuck with me. That's the kind of gap you only catch by actually watching real patients over time, not something you'd stumble on building in a vacuum. Congrats on getting Android live, that's a big milestone.
Thanks Nolan, that means a lot. You've put your finger on exactly why we did it this way round.
The blood-normal-but-scalp-not moment happened often enough in the clinic that it stopped feeling like an anomaly and started feeling like a pattern worth building a solution around. You're right that it's not something you'd design for on a whiteboard. It only shows up when you're watching the same people come back month after month and the numbers on paper don't match what you're seeing under the trichoscope.
Android live is the first proof point. With more than 600 clients and 18 months of trichoscope data behind it, the pattern wasn't a hypothesis by the time we started building, it was already established. The app is really just the natural next step, taking what we'd already validated in one salon and making it available to anyone, anywhere.
Appreciate you taking the time to read the whole thing.
@gerard_brandon you're welcome, I hope you can keep developing and upgrading the product Orga Hair so clients can experience the best
Already left an upvote to congrat, hope that you can support our AI Hive product too
@nolan_vu done, but also looked at AIHive and it looks impressive. It certainly would have been something I would consider were I back in my executive position
Everyone is being told to prompt AI. Almost nobody is being taught to lead it.
Prompting is one line and a hope. Leading is the whole loop. Knowing what you actually want. Saying it clearly enough that the work can't drift. Then judging whether what came back is the thing you meant. That loop is a leadership skill, and it does not get easier just because the code got faster.
That whole loop is what I'm building Tiflo to run with you.
It helps you shape a rough idea into a clear intent. It turns that intent into a contract your AI workforce builds to, so the goal is set before the first line of code. And at the end it does the judgment, checking the work against what you meant, and keeping an honest record of what it can prove, what needs your call, and what it could not verify.
You lead. The AI does the work. Tiflo keeps the two in line, start to finish.
I'm starting with software, because software has a right answer you can check against, so leading it well can be proven first. But leading AI is a skill, not a feature. The same loop travels anywhere a person hands a goal to a machine and has to trust what comes back.
Software first. The way of leading scales far past it.
If you lead people who build with AI, which is harder, getting the intent clear or trusting what comes back?
@wisegoat94 Thanks for sharing this, it's a good reframe honestly. "Prompting is one line and a hope, leading is the whole loop" is such a clean way to put it, saving that line.
Answering your question, for me the harder part is trusting what comes back, not getting the intent clear. I can usually write a clear brief, but validating whether the output actually matches what I meant takes way more discipline than people admit. That judgment layer you're building sounds like the missing piece most AI tools skip entirely.
hey i'm building: onlystoic.com
looking for early people who want to waitlist for it.
it's going to be an iOS app.
@divvsaxena Thanks for the heads up Divv, stoic-focused iOS app is an interesting niche, especially with how much noise there is in the mindfulness and productivity app space right now. Would love to know a bit more, what's the core habit or ritual you're building the app around? A one-liner about what makes it different would help waitlist folks like me get excited faster.
Rooting for the launch either way, stoic content done well ages really beautifully as a product. Just followed to keep an eye on the waitlist 🏛️
@nolan_vu Thanks man!
I'm doing a lot of research with other competitors right now.
I think the main focus will be on finding out the users mood and providing them with value according too it.
@divvsaxena Haha no worries man, happy to help. Doing competitor research first is smart, saves you from building something that already exists in a worse form.
Honestly user mood detection is underrated as a focus, most tools just chase features and forget that people interact differently depending on how they're feeling in the moment. If you can nail that value-add layer on top of it, you'll stand out from the noise pretty fast.
Good luck with the research phase, hope it leads somewhere solid. Upvoted and following along for updates. If you don't mind, I'd love your eyes on AI Hive too, we're building automation flows for smoother team workflows: https://www.producthunt.com/products/ai-hive
The issue may not be general visibility.
It could be that neuphlo is still too broad for people to understand why it deserves attention.
“Agentic workflow management” can cover many areas: dev teams, support teams, QA, marketing, campaigns, docs, onboarding. All of these may be valid, but trying to address them all at once likely makes it harder to stand out.
A sharper question might be:
which specific team would be genuinely annoyed if neuphlo disappeared tomorrow?
Based on what’s visible, two wedges seem worth testing first:
QA / test teams
because neuphlo includes test management, traceability, AI-generated test cases, and bug-task linkage.
Support teams
because the Intercom → severity → engineering task → help article workflow is very concrete.
Avoid showing the entire platform at the start.
For QA teams, focus on one workflow:
“Take this requirement, generate test cases, run them, and turn a failing case into a bug task.”
For support teams, focus on one workflow:
“Take this customer conversation, classify severity, create the engineering follow-up, and update the help article.”
Then observe:
whether it’s understood within 30 seconds;
whether they mention which tool it could replace;
whether they offer a real case to test;
whether they bring in a teammate;
whether questions about self-host / BYOM / EU hosting only come after the workflow feels useful.
If the reaction is only “cool platform,” that’s weak.
If one group starts sharing real workflow examples, that’s likely where visibility begins.
@riceroad Thanks for the thoughtful build story, "finding real founders with real unfinished validation problems" is such a real and honestly overlooked segmentation problem. Not everyone asking for feedback is actually stuck on validation, some just want positive reinforcement, some need positioning, some need launch support. Being clear about which pain you solve is half the battle.
Manually reviewing real founder posts to test the tool is honestly the right approach at this stage. It's slow but it teaches you what real vs performative feedback needs actually look like. Automation comes later.
Notchup
Building: xditto to solve personal productivity problem particularly situations where you need similar conversations with multiple individuals, but every conversation is different.
For: You can build your own voice agent, but many semi-technical folks would not do and may need an out-of-box setup. Which is what I am offering through xditto. E.g. Recruiters doing initial filtering of job applicants, VC analysts asking same initial filtering questions to inbound startup funding queries, mortgage brokers asking similar information to all borrowers.
Hardest part: Getting users to build custom capabilities for their clones
@maulik_sailor Thanks Maulik, xditto solves a genuinely interesting problem, "same conversation with different individuals" is exactly where recruiters, VC analysts, and mortgage brokers waste huge amounts of time. Out-of-the-box voice agents for semi-technical folks is a strong wedge because most voice agent builders assume the user is already technical.
The custom capability building challenge you mentioned is real. Most non-technical users don't know what capabilities to define upfront, so a guided onboarding with role-specific templates (recruiter template, VC template, broker template) would probably move the needle fast.