I'm going to tell you the honest version of this story, which means some of it isn't flattering.
I didn't have a clean insight moment. I didn't see a gap in the market and move decisively. I stumbled into the right product idea after building the wrong one first, and by the time TextSight worked the way it was supposed to, I'd rewritten the core scoring engine three times.
But the problem — the thing that wouldn't leave me alone — that was real from the beginning.
The False Positive Problem Nobody Was Talking About
It was late 2023. AI detection tools had been around for a while. GPTZero, Originality.AI, a few university-deployed systems. The discourse was mostly about catching students cheating.
I kept reading stories in Reddit threads, in education forums, in places like r/college and r/studentlife, that went like this: I wrote this essay myself. My professor ran it through a detector. It came back 87% AI. I'm facing an academic integrity hearing. I don't know what to do.
These weren't isolated. There were dozens of them. Hundreds, across different platforms.
Here's what bothered me: the tools gave a verdict. 87% AI. Case closed. But they gave no explanation. No "here are the specific patterns we found." No "here's what's making this read as AI." Just a percentage and an implication of guilt.
An 18-year-old kid, writing in English as their second language, using the formal academic structures they'd been taught, structured their essay the way their teachers told them to — and ended up in an academic integrity hearing because a pattern-matching tool said so.
That felt wrong. Not just imperfect — structurally wrong. A verdict without evidence isn't justice. It's not even accountability. It's just power.
The Insight Wasn't "Build a Better Detector"
My first instinct was wrong. I thought: I'll build a more accurate detector. Fix the false positive problem at the model level.
I spent about 6 weeks going down that path before I realized it was a dead end. Not because better accuracy was impossible — but because accuracy wasn't actually the problem.
Think about it this way. Even if you get to 97% accuracy, 3% of the people flagged by your tool are innocent. At scale — say, 10 million scans a month — that's 300,000 false accusations. Accuracy improvement doesn't solve the underlying problem. It just makes it statistically smaller while leaving the experience of a false positive exactly as devastating for the people it happens to.
The real problem was the binary verdict. Human or AI. Guilty or innocent.
Writing doesn't work that way. An essay written by a second-language student who used AI for grammar checking and then revised every sentence — what is that? It's not "AI-generated." It's not "purely human." It's somewhere in between, and the interesting question isn't which category it falls in. It's: how much of the distinctive human writing is there?
That's when the score idea came in. Not 87% AI. But 74 out of 100 on a human scale — and here are the specific phrases pulling you below 80. That's something you can actually work with.
Building It: The Ugly Parts
The first version of TextSight took me about 4 months to get to something I'd show anyone. I was building evenings and weekends, working out of Ahmedabad, using a laptop with barely enough RAM to run the models locally for testing.
The scoring engine was bad at first. I mean genuinely bad. It was flagging clearly human writing too often, and it wasn't surfacing the right vocabulary patterns. I'd test it on my own writing — things I'd written years ago, before AI tools existed — and score myself at 61. That's not great for credibility.
I rewrote the model training approach twice. The third version used a different approach to calibrating the baseline human distribution, and the false positive rate on known-human writing dropped significantly. That was the version I put in front of the first users.
The vocabulary highlighter came from a specific frustration. I wanted to be able to show someone: "It's not your whole essay. It's these 12 phrases. Here's what they are. Here's why they look AI-generated." The technical problem was identifying which phrases were statistically overrepresented in AI output relative to comparable human writing on similar topics — not just "AI words" globally, but contextually flagged patterns.
That part took longer than the scoring itself. It was worth it.
Who Showed Up First (It Wasn't Who I Expected)
I launched a very quiet beta in early 2024. No Product Hunt, no press. Just a link shared in a few subreddits and a Twitter thread explaining what I was building.
The first 50 users were almost entirely students. Specifically, ESL students and international students who had been flagged unfairly. They were using TextSight to understand their own writing — not to cheat, but to fix the specific patterns that were making their genuine work look AI-generated. They wanted to understand what to change before they submitted.
I hadn't fully anticipated that use case. I built a detector. But what these users needed was a diagnostic tool. The score was secondary to the vocabulary feedback — they wanted to know what to fix, not just that something was wrong.
The second wave of users came from freelance writers — people selling content to clients who had started demanding AI-free work. They needed to verify their own output and, again, identify what to revise before delivery.
HR professionals came third. Background screeners and recruiters who were manually checking cover letters and application essays were looking for a faster signal. That was the use case I'd anticipated least, and it turned out to be one of the stickiest segments.
The Moment It Clicked
There's one specific day I remember clearly. I was reading a message from a user — a student at a UK university, originally from India — who'd been through an academic integrity process and won. She'd used TextSight to understand what was making her writing flag, revised the specific sections it highlighted, re-submitted, and the revisions cleared the investigation.
She wrote: "Your tool didn't just clear me. It taught me how to write better."
That's the thing I hadn't fully articulated to myself until I read her message. The score model doesn't just tell you where you are — it creates a path. You know the destination (75+ on the Humanization Score). You know which phrases are the obstacles. The gap between where you are and where you need to be becomes a concrete, workable problem rather than a vague "your writing sounds AI."
That's what made me confident in the product for the first time. Not the accuracy metrics, not the user count. That message.
What's Still Not Solved
I'm not going to pretend everything works perfectly. Some things genuinely don't, and I think about them regularly.
Non-English detection is weak. TextSight works well for English. For Hindi, Arabic, Portuguese, Spanish? The model quality drops meaningfully. Given that my largest user base segment is international students — many of whom write in English as a second language but whose primary context is non-English — this is a real gap. I'm working on it, but it's a hard problem.
Voice content. As AI voice generation has gotten better, the question of whether a podcast or video script sounds AI-generated is becoming real. TextSight doesn't handle audio or video. That's the next frontier I'm thinking about.
The very sophisticated human-AI hybrid. When someone genuinely writes something, feeds it through an AI for light editing, then revises the AI's revisions — that's a mixed document. The current scoring engine doesn't have a clean answer for that. The vocabulary patterns are present but attenuated, and the score lands in the grey zone (41–60) where it should, but the explanation isn't as clear as I'd like.
I'm honest about these limitations with users. A score of 55 doesn't mean you're a cheater — it means your writing has patterns that overlap with AI output in ways the model can't fully resolve. That's a different message than "87% AI."
Where It's Going
TextSight crossed 1,000 users without any paid acquisition. That felt like proof that the product was solving something real.
The road from here involves a few things I care about a lot. Better model coverage — keeping up with GPT-5, future Claude versions, whatever comes next. Improved vocabulary diagnostics, especially for domain-specific writing (legal, medical, academic). And eventually, multilingual detection that works as well as the English engine.
The bigger picture is something I think about differently than I did at the start. I built a detection tool. But I think the category that actually matters is writing intelligence — tools that help people understand what makes their writing feel human or not, and give them the specific feedback to improve it. Detection is part of that. It's not the whole thing.
The false positive problem that started all of this — I haven't solved it completely. No one has. But I've built something that at least gives people a direction instead of just a verdict. That still feels like the right problem to be working on.
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