I Built Ed-Tech Before Ed-Tech Existed in India
In 2018, I started Pragmatic Leaders to teach product management in India. The category didn’t exist yet. Most companies were hiring for “marketing,” “sales,” or “operations” — PM was a Silicon Valley thing. I had 21 paying students across 3 countries and no funding. By the time Unacademy and BYJU’S were raising billions, we’d trained thousands and generated ₹4+ crores in salary hikes for students.
The insight: building before the market exists forces you to validate pedagogy instead of growth. That constraint became our advantage.
Market-Before-Product vs. Product-Before-Market
Most ed-tech companies in India built for a market that already existed. BYJU’S entered K-12 test prep — a ₹40,000 crore market. Unacademy entered competitive exam coaching — already massive. They optimized for distribution and unit economics in proven categories.
We built for a market that didn’t exist. Product management education in India in 2018 was not a category. There was no TAM to cite, no comparable to benchmark against, no playbook to copy.
When you build before the market, you can’t fake it. You can’t raise $50M and buy your way to product-market fit. You have to actually solve the problem first.
Why bootstrapping forces better pedagogy
If you bootstrap an ed-tech company in an unproven category, you will build better pedagogy than if you raise capital in a proven category.
Capital in a proven market optimizes for scale. You know the category works — the question is execution. Can you acquire cheaper? Convert faster? Retain longer? The pedagogy becomes a variable to optimize, not the foundation to validate.
Capital in an unproven market is a trap. You’ll spend it trying to create demand instead of validating that you can actually teach the thing. You’ll hire a sales team before you know if the course works. You’ll scale a mediocre product into a bigger mediocre product.
I couldn’t do that. I had 21 paying students and no investors. The only way to grow was if those 21 students actually learned product management and got better jobs. If the pedagogy didn’t work, I had no business.
So I built the pedagogy first.
The validation
I worked alone for the first year. Customized an LMS to deliver the course and gamify the learning. Watched every student’s progress. Saw where they got stuck. Saw what clicked.
The metric wasn’t revenue. It wasn’t NPS. It was: did they get the job?
Out of those first 21 students, 18 transitioned into PM roles or got promoted. Salary hikes ranged from ₹3L to ₹12L. That’s a 94% success rate on a sample size small enough to actually track.
That’s when I knew the pedagogy worked.
The technical shift
By 2019, I had a problem: I could teach 21 students well. I could probably teach 100 students well. But could I teach 10,000 students well?
The standard ed-tech answer is: record the lectures, sell access, scale horizontally. That’s not teaching. That’s distribution.
I made a different bet. I decided to build the platform and algorithms that could use the data we had from students. Individualized learning — not as a marketing term, but as an actual technical architecture.
Here’s what that meant in practice:
- Track where each student struggled in the curriculum
- Identify patterns across cohorts (e.g., “students from non-tech backgrounds struggle with API design”)
- Generate personalized problem sets based on performance
- Adapt pacing based on engagement and comprehension signals
This wasn’t LLM-powered. This was 2019. We built rule-based systems and basic ML models. But the principle was right: use data to make the course adapt to the student, not force the student to adapt to the course.
By 2020, we had 130 students in upfront-fee courses and 30 in ISA-based courses. We were adding 1.3 students daily — slow by VC standards, sustainable by pedagogy standards.
Cumulative salary hikes: ₹4.2 crores. Hours of training delivered: 30,000+.
What I got wrong
I thought the hard part was building the pedagogy. It wasn’t. The hard part was explaining why our pedagogy was different.
Every ed-tech company in India was claiming “personalized learning” and “industry-relevant curriculum” and “job guarantees.” We actually did those things, but we sounded identical in marketing. I didn’t know how to communicate the difference between a customized LMS and a data-driven adaptive platform. To a prospective student, they both just looked like “online course.”
I also underestimated how much the ed-tech boom would commoditize the category. By 2021, there were 15+ PM courses in India. Some were good. Most were recorded lectures with a Slack group. But they all charged ₹30k-50k, so we were competing on price instead of outcomes.
I should have built the brand earlier. I should have been louder about the salary hikes and the job transitions. I was too focused on the product and not enough on the perception.
Two models, two outcomes
| Market-Before-Product (Standard Ed-Tech) | Product-Before-Market (Pragmatic Leaders) |
|---|---|
| Raise capital to acquire users | Bootstrap until pedagogy is validated |
| Scale horizontally (more students, same content) | Scale vertically (better outcomes per student) |
| Optimize for CAC and LTV | Optimize for job placement and salary hike |
| Pedagogy is a variable to test | Pedagogy is the foundation to prove |
| Growth is the signal of success | Outcomes are the signal of success |
Both can work. But they produce different companies.
The first model produces Unacademy: ₹30,000 crores raised, millions of users, unclear pedagogy differentiation.
The second model produces Pragmatic Leaders: bootstrapped, thousands of students, ₹4.2 crores in salary hikes, 10,000+ professionals trained across programs.
I’m not saying one is better. I’m saying they’re optimizing for different things.
The question I haven’t answered yet
How do you scale individualized learning without destroying the individualization?
The data-driven approach works at 130 students. It works at 1,000 students. Does it work at 10,000? At 100,000?
At some point, the algorithms need more sophisticated models. The feedback loops need tighter instrumentation. The content needs to be modular enough to recombine dynamically but structured enough to maintain pedagogical coherence.
I thought I’d solved this in 2019. I hadn’t. I’d built a system that worked for the scale I was at. The next order of magnitude is a different problem.
This is why I’m building Ostronaut now. It’s the same problem — how do you deliver individualized learning at scale — but with better tools. Multi-agent AI systems that can generate, validate, and adapt content. Not as a replacement for pedagogy, but as infrastructure for it.
If you’re building ed-tech in an unproven category, bootstrap until the pedagogy works. Don’t raise capital to create demand. Raise capital to scale supply once you’ve proven the outcomes.
The mistake is thinking you can skip the pedagogy validation phase because the market already exists. You can’t. Students will pay once for a mediocre course. They won’t pay twice. And they definitely won’t refer their friends.
Are you optimizing for growth or outcomes? In the long run, only one of those compounds.