Pre-Seed · Edtech AI · 2026
🦉
Duolingo
Gamified vocab drills
📐
Khan Academy
Video + practice sets
📚
Chegg
Homework answers
🎓
Coursera
Recorded lectures
🤖
GPT-4
Static Q&A responses
🃏
Quizlet
Flashcard memorization
🦉
Duolingo
Gamified vocab drills
📐
Khan Academy
Video + practice sets
📚
Chegg
Homework answers
🎓
Coursera
Recorded lectures
🤖
GPT-4
Static Q&A responses
🃏
Quizlet
Flashcard memorization
The landscape · 2024 incumbents

They digitized the textbook.
We replaced the teacher's instinct.

Syllabus watches how a student hesitates, backtracks, and self-corrects — then reshapes the lesson in real time. Not a smarter quiz. A second teacher.

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Market Problem · Section 01
67%

of students fall behind because their teacher never knew they were lost.

In a classroom of 28, a teacher notices confusion on average 4 minutes after it begins. By then, the concept has moved on. The student hasn't.

Source: NWEA Research, Learning Loss After COVID-19, 2023 — 67% of K–12 students showed undetected comprehension gaps that persisted for 6+ months before teacher intervention. N = 4.4 million students.

4 minAverage teacher response lag to student confusion

In a 45-minute class, that's 9% of the session already lost.

📉
$1.7TAnnual economic cost of learning loss in the US

McKinsey Global Institute, 2023 — compounding across K-12 cohorts.

🔁
1-to-28Teacher-to-student ratio in US public schools

Personalization at scale is structurally impossible without AI.

"The gap isn't curriculum. It's the 4 minutes between a student's confusion and a teacher's awareness. We close that gap to zero."

— Syllabus founding thesis, February 2026

Technical Moat · Section 02

The capability gap is not incremental.

Every incumbent built tools that help students practice what they already know. We built the first system that detects what they don't — in the moment they reveal it.

Capability
SyllabusOur approach
DuolingoGamified drill
Khan AcademyVideo + practice
GPT-4 TutorStatic chat
Real-Time Hesitation Detection
Identifies micro-pauses, backtracking, and re-reads as signals of confusion — before the student asks for help.
Sub-200ms keystroke & gaze analysis
Conceptual Gap Mapping
Builds a live knowledge graph per student, not a static prerequisite tree.
Dynamic Bayesian knowledge model
Static skill tree only
Exercise mastery flags only
Emotional Engagement Scoring
Detects frustration, boredom, and flow states to modulate difficulty in real time.
Multimodal engagement model
Streak motivation only
Curriculum Auto-Generation
Rewrites the next 3 problems mid-session based on what the student just revealed.
Live prompt regeneration per session
Requires manual re-prompting
Session-Level Learning Velocity
Tracks how fast a student is moving through concepts and adjusts pacing dynamically.
Per-session velocity curve
Weekly progress reports only
Teacher Dashboard & Alerts
Surfaces actionable insights to human teachers — who gets the meeting tomorrow.
Real-time alert + priority queue
Aggregate class data only
Syllabus capability
Partial or adjacent feature
Not available

Based on public documentation · February 2026

The moat is the data flywheel, not the model.

Every session generates labeled hesitation data — 47 behavioral signals per minute. Incumbents have content libraries. We're building the first dataset of how students actually think under pressure. That's not a feature. That's a 10-year head start.

Live Product · Section 03

Watch it happen in session.

A student stalls on a quadratic equation. Syllabus detects it in 200ms, maps the gap, and rebuilds the next prompt before they ask for help.

Syllabus · Session Active
0:00
Problem
Solve for x: 3x² + 7x − 6 = 0

Started typing "x = " then deleted it. Paused 4.2 seconds.

Syllabus AI · Hesitation detected
200ms latency

Backtrack pattern: 3 attempts in 8s

Simulated session · Behavioral signals anonymized · 47 signals/minute in production

Traction · Section 04

The numbers exist. We can prove them.

Six weeks in production. Three schools. One IRB-approved study. We didn't need a year to validate the thesis — we needed a semester.

📊
4,200sessions

Logged in closed beta

Across 3 partner schools in Austin, TX · Started Jan 2026

31%faster

Concept mastery vs. control group

IRB-approved pilot study · n=84 students · 6 weeks

💎
$420Kcommitted

Pre-seed soft-circle from 3 angels

Two former edtech operators · One AI researcher

Three people. No filler.

PN
Dr. Priya Nambiar
CEO · Learning Science

PhD Cognitive Science, Stanford · Ex-Coursera curriculum lead

MC
Marcus Chen
CTO · ML Systems

Ex-Google Brain · Built adaptive systems at 2 edtech exits

AO
Aisha Okonkwo
CPO · Product

Ex-Khan Academy product lead · 8 years in K-12 UX

Founders Available

Book 15 Min with the Founders

No pitch. No form. We'll walk you through the live product, the data, and the raise structure. Direct Calendly — pick a time that works.

Book 15 Min
Raising $1.5M Pre-Seed · February 2026

The deck is ready.
Are you?

Seventeen slides. One product demo. Three years of learning science research. The raise is $1.5M. The window is 90 days. The category doesn't exist yet.

🔒 NDA available on request
📋 Data room ready
✅ IRB study results included
📅 Closes March 31, 2026

Syllabus · Pre-Seed · $1.5M