EveryEssay isn’t trained on guesses. It’s trained on people who already won. Instead of hallucinating what reviewers want, the AI learns from real alumni essays, acceptance letters, and proven evaluation rubrics. No source, no output. The AI stays locked until enough human-verified wins unlock it. What you get isn’t a template or polished fluff—but the exact logic behind essays that passed. Not AI writing for you. AI backed by human proof.
Hey Product Hunt 👋
Maker here.
We built EveryEssay because applying for scholarships felt like playing a game where nobody explains the rules.
When I applied, I kept asking one question:
“What does a winning essay actually look like?”
Not the motivational fluff. Not the LinkedIn humblebrag. The real one.
Online examples were generic.
AI tools wrote confident nonsense.
Private consultants charged more than the tuition itself.
It wasn’t just confusing—it felt unfair.
So we built EveryEssay around a simple idea:
If someone already won, their knowledge shouldn’t disappear.
Instead of teaching AI to “sound smart,” we let alumni and successful applicants train it with real essays, acceptance letters, and evaluation rubrics. No source? The AI stays locked. No guessing allowed.
This is our attempt to level the playing field—
so access to education isn’t a luxury tax anymore.
Would love your thoughts, questions, or brutal feedback.
We’re building this in public. 🚀
— Nabil & the EveryEssay team
We’re finally getting into the first wave of AI tools that will also allow or require us to think through processes and develop final products WITH AI assistance. Vibe coding is cool for all of 10 minutes then my brain feels numb from lack of use.
Congrats on the launch! Training on winning human briefs is a strong and differentiated angle. I like that this isn’t positioned as generic AI writing, but as learning from real outcomes that already worked. That should resonate a lot with job seekers who care about results, not just polish. Curious how you ensure originality while still leveraging patterns from successful essays.
First-gen here. Spent nights rewriting the same 500 words. This feels more fair. Like the no-source lock. I'm curious how you handle consent from alumni and keep outputs from sounding like a copy of past winners.
I like the mission here.
How do you handle permissions and copyright for essays used to train, so you stay on the right side ethically?
Hi Nabil👋this is a solid product,Super clean landing page,love the typography choices and the calm, confident layout.
One small UX note though: the horizontal auto-scrolling testimonials feel a bit slow, so it takes effort to notice new social proof. Slightly increasing the speed or adding subtle user control could make that section pop even more.
Overall, really solid execution 👏
This is a game changer for transparency. The 'no source, no output' philosophy is exactly what's needed to fix AI hallucinations in EdTech. 👏 One question on the supply side: How do you incentivize alumni to share their winning essays and rubrics? Do they get a cut of the revenue?
Really interesting concept. Curious how you make sure the insights stay relevant across different program and situations, especially as expectations and evaluation styles keep changing over time?
The 'Moneyball' approach to essays is brilliant. I'm curious about the dataset bias though: Is it currently US-centric (Ivy League/Common App), or does it also cater to UK/European university application styles which are usually more academic and less 'story-driven'?
