r/startup

Three startups. Three times I built something people said they wanted. Three times they didn't actually buy it.

The pattern was obvious. I was asking "would you use this?" and collecting yeses like they meant something. They didn't.

The problem is structural. When you ask someone if they'd use a product, they're predicting their future behavior. Humans are awful at this. We're biased toward yes because we don't want to be negative, and the cost of saying yes is zero, they're not actually committing to anything.

Contrast that with asking about behavior that already happened. "How do you handle this today?" Nobody can lie about what they're already doing. That's real, verifiable behavior.

These are the five questions I run through every customer discovery conversation now:

- How do you solve this today? → Your actual competition

- What's the most frustrating part? → Your positioning, in their words

- What does it cost you? → Whether there's a business here

- What have you already tried? → Active seekers vs. passive complainers

- What does ideal look like? → The outcome they want, not the feature they imagine

The third question is probably the most important. If someone can't tell you what the problem costs them, in real time or real dollars, it's probably not painful enough to pay to fix. That's the filter that saves you from building vitamins.

I now do a minimum of 25 conversations before writing a single line of Vibecode. At around conversation 15-20, you start hearing the same language. The same metaphors. The same workarounds. That's when you know you've found something real.

One thing that changed how I listen: I stopped pitching. Most founders do "discovery" where they pitch for 15 minutes and ask for feedback for 5. That's backwards. You should be quiet 80% of the time.

What's the question you've found most revealing in these conversations?

I'm CTO of a govtech startup. About a month ago we received the contract for a pilot with our first customer. My co-founder (CEO) wanted to have the contract revised by a lawyer since it is our first contract and didn't want to take any risk. In fact, there are a couple of clauses in the contracts that are favouring our client too much, so I do believe that a revision was necessary.

What frustrates me is that it has been a month since we received the contract and we haven't sent back the revised contract yet. That's because of how long it took to engage a lawyer expert in these things and the lawyer is not done yet with the revision. I thought the whole thing would take a week, 10 days max.

The contract is very important for us because it is all about market validation (and it's a pretty fat contract too since it includes not only the pilot but also what follows after that). I think taking so long for a contract revision might jeopardize this opportunity and is poor execution.

What do you guys think? Is it normal to take so long to send back a contract? Do lawyers normally take that long? What should have we done instead?

been auditing pricing pages for a while now and cross-referencing against the public conversion benchmarks, and the picture is pretty grim for classic freemium.

the numbers:

• first page sage aggregated data from 86 saas companies (2022-2025) and found an average freemium-to-paid conversion of 3.7%, going as low as 2.6% in edtech.
• chartmogul's 2026 report (200 products, study run january 2026) puts "good" freemium conversion at 3-5%. that's the good tier. most are below it.
• meanwhile free trials that require a credit card see roughly 30% free-to-paid, more than 5x the ones that don't.

so you're running infra costs for 95-97 free users to convert 3-5. that math worked when compute was cheap and you weren't paying per AI token. it doesn't anymore. every "ai-powered" freemium tier is now a cost center with a login screen.

the interesting part: reverse trials. new user gets the full premium product for 14 days, then gets downgraded to a free tier instead of locked out.

• only 7% of products in the chartmogul study run one, so it's still early.
• but they convert comparably to standard free trials (good: 4-6%, great: 8-12%) while keeping the top-of-funnel signup volume of freemium.
• elena verna (growth at miro, amplitude, lovable) has been pushing this model hard, and toggl runs it — full premium on signup, then downgrade to a limited free plan.
• the psychology is just loss aversion. losing a feature you've built a workflow around hurts way more than never having it

freemium's one remaining defense is the long tail — free users who convert 6 months later. reverse trial keeps that AND forces every signup to actually see the paid features. pure freemium users often never even discover what's behind the paywall.

my take: if your free tier exists because "that's what everyone does," you're subsidizing tourists. the 7% adoption number on reverse trials is the opportunity. it won't stay at 7%.

not saying freemium is dead for network-effect products (slack-type stuff still works). but for the average b2b tool, the default is shifting and the data's been public for months.

​

One of the first ideas I had while planning our AI verification prototype was to add a confidence score to every result.

The more I thought about it, the less I liked the idea.

Imagine an AI says:

"EBITDA = $12.3M (96% confidence)"

What does 96% actually tell the person reviewing a borrower package?

It doesn't explain:

where the number came from,

whether another document reports a different value,

whether the calculation follows the covenant definition,

or whether the evidence is complete.

A high confidence score can easily become another thing people trust without understanding.

So we removed it.

Instead, we're experimenting with something much simpler:

Every important financial claim should answer four questions:

Where did this value come from?

Can I open the source immediately?

Does another document disagree?

If it's calculated, can I reproduce the math?

Maybe confidence scores are useful in some applications.

For the kind of workflows we're exploring, I'd rather help someone verify an answer than persuade them to trust one.

I'm curious how others think about this.

If you're building AI products, do you expose confidence scores to users, or have you found better ways to communicate reliability?

I'm building a tool to help salespeople improve their sales communication. Recently, I was talking to a founder, and he said:

"Imagine this is a self-improvement product. Most individuals won't buy it. But companies that want their employees to improve their communication will."

That completely changed how I thought about my product.

I shifted from targeting a large audience to a much smaller, higher-value segment. Instead of trying to convince thousands of individual users, I'm now focusing on the people who have the budget and a stronger reason to buy.

I also realized I don't want to spend too much time explaining what my product does. The right audience should immediately understand the value.

My takeaway: your real customer might not be your end user. Sometimes, it's the person who benefits from helping the end user improve. You don't always have to sell directly to the people using the product.

You don't need the perfect plan from the beginning. Sometimes, talking to a few people is enough to completely change your direction.

yo guys! I am 16 years old, I have been in the SaaS/AI world for about a year now. I just recently got into the app game about a month ago because i figured that b2b was way too boring for me. A few days ago my first app got approved to the app store! this is so crazy, because i just had the idea for it a few weeks ago. At this point I am just looking for some feedback, my plan is to get feedback from 10 or so people and then launch after I have fixed any errors that they point out.

My app is called Doomed. It helps people stop doomscrolling, before you open tiktok, instagram whatever, you have to wait for 30 seconds, and then after the 30 seconds is up you get roasted, the cool thing is that the roasts change per time of day, so at 9AM the roast might say that you should not start your day this way or whatever, and then at 2AM it will tell you to go to sleep. If anyone wants to give it a try, or even just take a look at the screenshots and copy pls let me know in the comments! I will give free access to whoever does.

thanks!

Hey all, we made a haircut visualization tool to cut down on miscommunication in the barbershop by generating a photo of you with the cut you want from three angles. We hope to officially launch soon and would love any feedback/early testing! It can be found online by searching trimkit.app, thank you for your time and good luck with all your startups!

Over the past few weeks, I've been building a prototype that checks AI-generated financial claims against source documents.

I expected the hardest part to be the language model.

It wasn't.

The hardest part has been defining what "correct" actually means.

For example, imagine two documents in the same credit package:

A covenant certificate reports EBITDA as $12.4M

The management accounts report $11.9M

Neither document is necessarily "wrong."

One might exclude restructuring costs. The other might use the covenant definition from the credit agreement.

An AI can extract both numbers perfectly and still leave you with the real question:

Which definition should be used for this specific decision?

That made me realize something:

In many business workflows, the challenge isn't generating answers.

It's defining the rules that determine which answer is acceptable.

The AI isn't always the weakest link.

Sometimes our own business processes are.

For those of you building AI products:

Have you found that defining business rules was harder than building the AI itself?

I'd be interested to hear examples from other industries.

Me and my team built a full erp and accounting software to simplify the admin side of running a business significantly, primarily for service businesses (works for inventory based too), i am looking for business owners that are willing to test and give us feedback over the course of a year, we will handle the implementation and onboarding all for free.

Over the last few months, I've been thinking about one question:

Why do we trust AI answers simply because they sound confident?

In many domains, that confidence is harmless.

But in finance, a single incorrect number can influence lending decisions, covenant monitoring, portfolio reviews, or risk assessments.

The problem isn't that AI makes mistakes.

Humans do too.

The problem is that today's AI systems rarely show why a financial claim should be trusted.

That realization led me to start building AutoFlow.

We're not building another chatbot or AI wrapper.

We're building a Credit Evidence Engine that verifies eligible financial claims against source evidence, calculation rules, and document consistency.

Our first prototype is intentionally narrow.

It focuses on credit packages, borrower financial statements, covenant calculations, and exception detection.

If two documents report different EBITDA values, the system shouldn't silently choose one.

It should expose the contradiction.

If a leverage ratio is calculated, it should be traceable back to the covenant definition and supporting evidence.

I'm sharing this journey in public because I believe trust is earned through transparent decisions, honest limitations, and continuous learning—not confident marketing.

I'm still in the prototype stage, and I expect many assumptions to be challenged.

That's exactly why I'm building in public.

Question for other founders:

When you're building trust before you have customers or production case studies, what has mattered most in your experience—clear scope, technical proof, transparent progress, or something else?

I'd genuinely like to learn from your experience.

Angel investor here, looking to invest in early-stage startups.

I typically invest in B2B companies, location agnostic, with an existing MVP/product.

I can write a ton of things about what I bring as an investor, but I guess that'll count as promotion.

I have a mind-blowing idea and a fully working Android app prototype. Everyone from niche target audience, let's say amateur/street soccer players, are shocked by wow-effect, and say "Brilliant!", "Stunning!", "Game changer" and so on. They are ready to pay $5/mo right now.

I can't release it because of sanctions, but I had found lifelong friends (in the Ukraine, ironically) so it is not really a problem.

I might need initial funding -(*) there are already some huge players (apps) with loyal audience and paid subscripters. They have offices, and software development departments. It's a matter of a couple of days for them to copy my concept.

So I need to break in fast, invest in promo to go viral and become a de-facto standard, like GoPro in one's best years. What could be the solution if I am poor? Russia is isolated so there isn't any venture funds or so on.

My suggestion is to make a flawless product. Spend a fuckload of time and increase the test cohort from 30 to 300 people. Show them, if the reaction is just as passionate -(*) release and spam to famous Tik-Tokers or whoever are trendsetters in target demographic (that is roughly 13-23 y.o., their influencers are subject to research). It should be highly viral, if anyone will notice (imagine a phone vibrating in your pocket that teaches you how to play soccer good). But I am a realist and sure that said influencers never even open DM. And I can't send a business inquiry because of lack of money.

Are there even possibilities to start with?

*fuck you, AI filters, it's a place for em-dash, not hyphen

It is not a promo, or soliciting of any kind, I guess nobody in right mind is going to mess up with Russia, nor do I want this.

We are working to build a tech agency (something bigger in the SAAS field in the long term tho). But we don't have a portfolio yet. After seeking advice from some people who own branding agencies/SMMAs etc, we have planned to build entirely for free for our initial projects just to gain experience.

​We are trying to find people in the tech space who need help with their frontends or landing pages, and we want to do the work for free to get established.

​Let me know if you have any suggestions on the best platforms, communities, or strategies to find these early opportunities.

For a long time I am working as a UI/UX designer. Blogging was just a hobby I picked up around 2016.

Over the years I ended up with a few content sites, and one of them is about astrology.

Every day there's a new daily horoscope. Every week, a weekly horoscope. Every month... you get the idea.

It got to the point where I was spending more time updating content than actually growing the site.

When AI tools started getting good, I wasn't thinking that I'm going to build a SaaS.

I was just thinking like how can this automate the parts I hate doing?

So I started experimenting with Lovable.

First I automated the horoscope updates.

Then I automated article research.

Then writing.

Then images.

Then publishing to WordPress.

Little by little, the whole workflow disappeared.

The interesting part is that I never set out to build a product. I was just solving my own problem.

After using it for a while on my own sites, I realized other people probably have the exact same bottleneck. Not a lack of ideas or motivation, just too many repetitive tasks between "I should write this" and "it's finally published."

Then realized that it can be for any CMS or custom website, not just WordPress.

That's when I decided to turn it into a real product.

One thing this experience taught me is that a lot of good startup ideas don't come from brainstorming. They come from getting annoyed by the same problem over and over until you finally decide to fix it.

Curious if anyone else here ended up building a product almost by accident because you first built it for yourself.

And of course, since I built it for myself first, when I added the pro version and connected the payment, I was my first paying customer 😅

On one side - I hear so many startup founders complaining about lack of startup ecosystem support in India.

This side is more vocal.

But on another side - as an incubator owner & working with VC, Angels - I hear their views as well which seems rational that most startups are not ready, practical, equipped.

I see merit on both sides & wish to know how to bridge this gap better - any practical ideas welcome.

demand isn’t driving the economyy.
if you want to know if your startup will succeed you shouldn’t make something people want. you should make something that gives new capabilities.
there is infinite demand for teleportation but no matter how much people want it they are not getting it why because nonody was able to supply teleportation devices. so people use next best thing which is variants of transportation.
many people here blame marketing or distribution for their failed startup but look how fast products spread when they introduce a new capability? barely any marketing needed. just spreads like wildfire

A few companies in and I've seen all the playbooks and frameworks. It still comes down to sell harder and hit the number.

Has anyone actually worked under a GTM that was real strategy and not just pressure?