What is My Speaking Score's Moat? A Deep Dive into Our Competitive Advantage

Introduction

In the crowded world of TOEFL prep, most platforms offer similar solutions—coaching, practice tasks, and generalized tips. But My Speaking Score (MSS) is different. We aren’t just helping students practice—we’re engineering a new way to predict and improve scores using data. This blog post unpacks the moat that protects MSS from competitors—and why that moat gets deeper every day.

What Is a Moat?

In business strategy, a moat is the sustainable, hard-to-replicate advantage that protects a company from competitors. It’s what keeps others from storming your castle (see Reforge founder Brian Balfour's The Next Great Distribution Shift for more on moat and how AI is transforming distribution).

At MSS, our moat consists of four deeply interconnected layers:

  1. Proprietary Data & AI Models
  2. Category Ownership
  3. Strategic Differentiation
  4. Personalized Insight

Together, these form a compounding, self-reinforcing engine of defensibility.

Moat Layer Description Why It's Defensible
1. Proprietary Data & AI MSS has access to 100K+ real TOEFL Speaking responses, scored with ETS’s SpeechRater™ and e-rater®. This dataset powers VoX, our proprietary AI that predicts scores with a 95% confidence interval. No competitor can replicate this dataset or the real-world scoring context it offers. As users engage, our model improves—creating a self-reinforcing feedback loop.
2. Category Ownership We’re defining a new category: Data-Powered TOEFL Speaking Prep. We shift the narrative from "teacher help" to "data-guided independence." By creating a category, we shape user expectations and own the mental real estate. MSS becomes the default name in predictive, AI-first TOEFL prep.
3. Strategic Differentiation While most platforms begin with training or coaching, we focused first on score prediction accuracy. Now, our upcoming training platform (Scorpion) is built atop a diagnostic engine. Others start with guesswork. We start with data. That gives us a precision advantage that scales with every user interaction.
4. Personalized Insight Our platform identifies not just what your score is, but why it’s that way—analyzing fluency consistency, filler words, unnatural pacing, and template dependence. Generic feedback engines can’t match our precision. We’re building trust by showing users what *matters most* to improve.

Why This Matters Right Now

TOEFL prep is changing fast. Platforms built only around human coaching are beginning to feel outdated. Test-takers are demanding personalization, speed, and clarity. They're demanding community.

MSS is already there.

Because we don’t just help users improve—we show them exactly where to focus, using hard numbers and trusted scoring systems.

FAQ: Common Questions About Our Moat

Q: Can’t competitors build a similar model using public data?
No. Public datasets lack authentic, scored TOEFL Speaking responses. MSS’s dataset—scored with SpeechRater and e-rater—is uniquely rich, and our AI is trained specifically on TOEFL success signals.

Q: What if a major brand like ETS or Duolingo builds this?
They might try, but they’re slower to move, bound by legacy systems and bureaucracy. We’ve already built what others haven’t even scoped. And we're evolving it every week.

Q: Why didn’t you start with courses or training?
Training without diagnostics is just guessing. Our strategy has always been to start with truth—real data, accurate scores—and build intelligent training on top of it.

Q: Is this moat sustainable over time?
Yes. The more users submit responses, the smarter our system becomes. As we layer in AI coaching and personalized curricula, our edge deepens. The moat compounds with every click.

Conclusion

My Speaking Score isn’t just some random TOEFL Speaking prep tool. It’s a data engine. A diagnostic platform. A transformation assistant. Our moat is not one thing—it’s a full-stack strategy powered by data, trust, and results.

Want to see what a real moat looks like in action? Get your first SpeechRater report here →