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A personal STEM tutor for every student.

MindNova is an AI tutor combined with a live physics simulation lab — an end-to-end learning platform that teaches American K-12 students to think like engineers, not memorize like test-takers. We close the STEM gap by making world-class tutoring available to every student for the price of a Netflix subscription.

The opportunity

The Smithsonian Science Education Center estimated 3.5 million STEM jobs to fill by 2025 — and the pipeline gap persists. About 7 in 10 8th graders score below Proficient in math (NAEP 2024). Private tutoring at $50–150/hour is out of reach for most families. AI in K-12 EdTech is growing 39% per year toward a $5.2B segment by 2032, and almost no one is doing it well. Khanmigo (AI tutoring) is broad but shallow; Brilliant (STEM rigor) targets adults and older teens. MindNova owns the unowned quadrant: deep AI personalization × deep STEM simulation.

What we built

A platform of 11 integrated modules spanning a Socratic AI tutor, a live physics simulation lab, an adaptive difficulty engine driven by 30+ behavioral signals per student, a teacher intelligence dashboard, gamified mastery (XP, streaks, badges), and a built-in Women-in-STEM career track. The MVP is in active development; a working tutor prototype and three physics simulations run in the development environment today. Domain mindnova.academy is secured. Founder is relocating to the U.S. full-time.

Proof points

The ask

We are raising a $500K seed round to fund a lean, founder-led runway from MVP to a Q3 2027 Series A (12–15 months post-close), reaching ~$400K revenue in 2027 and $3.8M by Year 5 (2030). Funds deploy 50% product · 35% growth and pilots · 15% curriculum/compliance/ops.

Investment Highlights

Why Now

• AI inference cost collapse. GPT-class tutoring that cost $5/student/hour in 2022 now runs at sub-cent cost per session. The unit economics for free-tier STEM tutoring only became viable in 2024–2026.
• Teacher shortage is structural. 55,000 STEM teacher vacancies across U.S. K-12. Schools must adopt AI augmentation or lose the next generation of engineers.
• Federal urgency. The CHIPS Act, NSF priorities, and DOE workforce programs all flag the same gap. Title I funding plus Women-in-STEM grants create the policy tailwind.
• Parental willingness-to-pay. Post-pandemic learning loss has parents actively shopping for after-school AI tools. The $15/month price clears every objection.

Who we are.

Corporate Profile

Mission

To give every American student a world-class STEM tutor — and to close the gender gap in engineering by building Women in STEM into the product from day one.

Vision

To become the default AI learning platform for STEM in American K-12 — the system that teaches the next generation of engineers, scientists, and innovators to think, not memorize. Long-term, MindNova is the workforce pipeline for the country's most strategically important industries: semiconductors, clean energy, aerospace, biotech.

Values

• Teach, don't test. The product refuses to give answers. It teaches reasoning.
• Teachers first. Every product decision starts with: does this make a teacher's job easier?
• Earned trust. No hype, no shortcuts on safety. FERPA, COPPA, and parental consent are non-negotiable.
• Equity by design. Women in STEM is not a feature flag — it is a foundational track.
• Ship and learn. Weekly classroom feedback loops. Annual product overhauls based on what teachers actually use.

Company History

Product Lines

1. MindNova for Students (B2C — primary revenue line)

The student-facing platform: AI Socratic tutor, simulation lab, adaptive learning engine, mastery dashboard, gamified XP and streaks, Women-in-STEM career tracks. Web-first MVP launching Q2 2026; iPad-optimized web app Q3 2026; native mobile (React Native) Q4 2026. Freemium with $15/month Plus tier.

2. MindNova for Teachers & Schools (B2B)

Teacher dashboard with class roster, mastery tracking, at-risk-student alerts, AI-generated lesson differentiation, and automatic standards alignment (NGSS, Common Core). Free for individual teachers. School-tier pricing at $5/student/month (minimum 50 students = $250/month). Title I funding eligible — schools pay nothing out of pocket.

3. MindNova for Workforce (Corporate L&D)

STEM workforce pipeline product for enterprise sponsors. Designed for aerospace, defense, semiconductor, and clean-energy companies that fund internship and pre-hire training programs. $50/seat/month. Sponsored pilots from 2026; GA Q3 2027; first standalone contracts 2028. Initial interest via inbound from corporate sponsors of school pilots. No customer commitments to date.

Intellectual Property

Proprietary tutor prompt library + Socratic question trees + hint ladders covering ~1,400 STEM concepts (grades 6–12). Browser-native simulation library (physics, electronics, energy-systems). Bayesian student-model architecture using 30+ behavioral signals. MindNova™ trademark applications in process (U.S. classes 9, 41, 42). Domain mindnova.academy secured.

Governance, Cap Table (Post-Seed) & Operations

Post-seed cap table: Founder Rozlana Yergaliyeva 82% · Seed investors 10% · Employee stock option pool (ESOP) 6% · Technical advisor 2%. Operations: U.S.-based once founder relocation completes. Remote-first engineering team during seed phase; curriculum and partnerships roles U.S.-based for in-person district work (NSTA, ASCD, ISTE).

America has a STEM crisis.

Four broken loops

Loop 1 — The student can't get help when they need it.

A 7th grader hits a wall on quadratic equations Tuesday night. The teacher is unavailable until Friday's office hours. The parent doesn't remember the method. By Friday, the student is two lessons behind and has internalized "I'm bad at math." This compounds every week for the next eight years. The single biggest cause of the STEM dropout funnel is not capability — it is the latency between confusion and help. Today, that latency is days. With MindNova, it is seconds.

Loop 2 — The teacher is grading instead of teaching.

The average U.S. middle-school STEM teacher spends ~11 hours/week grading and ~6 hours/week on differentiation planning — time that should be coaching. With 55,000 vacancies and rising class sizes, the system is structurally incapable of giving every student individualized attention. AI must absorb the grading and differentiation load so teachers can do what only humans can do — mentor.

Loop 3 — The family can't afford one-on-one tutoring.

Wyzant and Tutor.com pricing runs $50–150/hour. The Khan Academy + Brilliant + IXL bundle costs ~$50/month but offers no personalized tutoring depth. The result: affluent families buy tutors; everyone else is left behind. The STEM gap is fundamentally a tutoring access gap. AI tutoring at $15/month closes it.

Loop 4 — Girls leave STEM at the middle-school transition.

Only 26% of U.S. engineers are women. The leak point is well-documented: 6th–8th grade. Girls report that STEM subjects feel "for boys" because the teaching examples, role models, and career pathways shown to them are dominated by male engineers. This is not a pipeline problem at the university level — it is a representation problem in the textbooks at age 11. A platform that surfaces female engineers and Women-in-STEM career narratives natively in the curriculum is the highest-leverage intervention available.

Why existing solutions fall short

The cost of inaction

For the student

Compounding learning loss. By 11th grade, a student who fell behind in 7th-grade algebra has typically been counted out of STEM tracks entirely. The lifetime earnings cost of dropping out of the STEM pipeline at age 13 is estimated at ~$1.2M per student.

For the teacher

11 hours/week of grading. Six hours of differentiation planning. Burnout rates above 50% within five years of credential. The system is producing the teacher shortage by overworking the teachers it has.

For the country

3.5 million STEM jobs the U.S. needed filled by 2025 (Smithsonian estimate) — a gap that persists. Strategic competitiveness in semiconductors, clean energy, AI, biotech — all tied to a domestic STEM pipeline that is leaking at the K-12 layer. CHIPS Act money does not produce chip engineers if 7th graders quit math.

For girls

The gender gap in engineering compounds across decades. Every girl who is told (implicitly) at age 11 that engineering "is not for her" is a loss to the field, the economy, and to her own lifetime trajectory.

A personal STEM tutor for every student.

MindNova comprises six student-facing pillars of the 11-module platform architecture — an AI Socratic tutor, a live simulation lab, an adaptive engine, teacher intelligence, gamified mastery, and a Women-in-STEM track. Together they form the first end-to-end AI learning system built specifically for the K-12 STEM crisis.

The six pillars

The student experience

A 9th grader opens MindNova at 9 PM, stuck on conservation of momentum. She types: "why does the heavier car push the lighter car backward?" The tutor replies not with an answer, but with a question: "Let's start with what you already know. If two cars collide and one stops moving, what happened to its energy?" She thinks. Tries an answer. The tutor responds with a graduated hint, then drops her into a live simulation: two carts on a frictionless track, mass sliders on each. She drags the masses. Watches what changes. Five minutes later she understands momentum conservation — not as a formula, but as a thing that is true about the world. She earns 40 XP, hits a 7-day streak, and the platform suggests she try the next concept: collisions in 2D.

That is the product. That is the loop we are scaling.

The teacher experience

A 7th-grade physical-science teacher in a Title I middle school opens MindNova Monday morning. Her class roster shows all 28 students. Three are flagged red: at-risk based on last week's work patterns. She clicks one — Marcus, falling behind on equations. MindNova shows her exactly which concept Marcus is stuck on (cross-multiplication), what hints he has tried, and recommends a 15-minute small-group intervention with two other students at the same gap. She does not grade a single worksheet. She runs the intervention. By Friday, Marcus is back on track. The platform did not replace her — it gave her superpowers.

The 11-module platform architecture

Product Tiers & Pricing

Workforce (Corporate L&D)

Workforce — $50 / seat / month. Internship and pre-hire training tracks. Branded portal. Skills-passport export. Reports to corporate L&D leader.

Built to teach, not test.

System architecture (high level)

The Socratic AI architecture

Six-step pipeline per turn:

1. Student utterance arrives (chat, voice, or worked solution).
2. Concept grounding + state retrieval — active concept identified from the ~1,400-node graph; mastery state, prior attempts, hint history loaded.
3. Refuse-to-answer prompt — Socratic posture: ask questions, offer 4-level graduated hints, never directly answer until the hint ladder is exhausted.
4. Curriculum retrieval (RAG) — relevant passages + worked examples retrieved.
5. LLM generation + validation against safety + grounding checks.
6. Mastery update + next-best-action — student model updated; next concept or simulation surfaced.

Unit cost. ~$0.04 / tutor session (5–10 turns) · ~2 sec P50 latency · 82% gross margin at scale · 99.5% uptime target.

The adaptive engine

The student model is a Bayesian knowledge-tracing layer with 30+ behavioral signals: response correctness, time-to-respond, hint usage, retry pattern, simulation interaction depth, emotional valence in language, error type, prior concept mastery, day-of-week activity, streak status, and many more. The next-best-concept selector is a contextual bandit that balances learning growth against engagement (frustration risk). Every student carries a private model that improves the platform's recommendations specifically for them — and the aggregate population model improves the platform for everyone.

The simulation engine

Browser-native, no plugin, no install. Built on WebGL with a deterministic physics layer so simulations are reproducible and testable. The first three simulations (kinematics, electrical circuits, energy conservation) ship at MVP. The roadmap expands to ~25 simulations across mechanics, electromagnetism, thermodynamics, wave physics, and chemistry by end of 2027.

Compliance & Safety Architecture

• FERPA-compliant data model — student PII isolated, row-level security, audit logging on every read of student data, district-level data residency support.
• COPPA-compliant onboarding — verifiable parental consent flow for users under 13, no behavioral advertising, no data sale, ever.
• SOC 2 Type II — engagement begins Q4 2026, target completion Q3 2027.
• AI safety — refuse-to-answer prompting, curriculum-grounded retrieval, weekly red-team prompts run against tutor, teacher review queue for flagged interactions.
• Bias audits — adaptive engine outputs audited quarterly for disparate impact across demographic groups.
• Accessibility — WCAG 2.2 AA compliance, screen-reader support, dyslexia-friendly typography option, high-contrast mode.

Engineering practices

• Two-week sprints, daily standups, Friday retro.
• Trunk-based development with feature flags. No long-lived branches.
• Pre-commit hooks for lint, type-check, secrets scan.
• End-to-end test suite for critical learning flows (tutor → simulation → mastery update).
• Observability: structured logs, error tracking, learning-analytics pipeline.
• Weekly classroom feedback loops — teachers tag interactions that worked or did not.

Technical risks & mitigation. LLM provider outage → multi-provider gateway (OpenAI, Anthropic, Google) with auto-failover. Hallucinated concept → curriculum-grounded retrieval, mathematical correctness checks, teacher review queue. Weak Chromebook performance → WebGL with graceful 2D-canvas fallback. School Wi-Fi blocks API → Cloudflare edge proxy with school-friendly IP range. Data residency → multi-region storage Year 2; district-level isolation opt-in.

$83B market. AI segment growing 39% a year.

U.S. K-12 EdTech is the world's largest education market. The AI segment is the fastest-growing slice — and almost no one is doing it well. MindNova owns the unowned quadrant: deep AI personalization × deep STEM simulation.

Market sizing

Market scale at a glance

Market growth drivers

• AI inference cost collapse. Per-token LLM costs have fallen ~50x since 2022, making free-tier and freemium economics viable for the first time in K-12 EdTech.
• Teacher shortage. 55,000 STEM teacher vacancies. Schools must adopt AI augmentation or lose the next generation of engineers.
• Federal urgency. CHIPS Act, NSF priorities, DOE workforce programs all targeting the same STEM pipeline gap.
• Title I capacity. $18B/year in Title I funding; evidence-based supplemental instruction (incl. tutoring software) is an allowable use.
• Parent willingness-to-pay. Post-pandemic learning loss has parents shopping for after-school AI tools — $15/month is impulse-buy territory.

Customer Segmentation

Segment 1 — The engaged student / parent (B2C, primary 2026)

Segment 2 — The classroom teacher (acquisition layer, free forever)

Free teacher tier is the lead-generation engine. 150 teachers in 2026 means ~4,500 students touched. Teachers are not the customer — they are the channel.

Segment 3 — The school district (B2B, primary 2027)

Segment 4 — The corporation (B2B Workforce, 2027+)

Beachhead + geographic expansion

Beachhead is two-pronged: (a) STEM-strong metros (Seattle, SF Bay, Austin, Boston, Research Triangle) — highest parent willingness-to-pay; (b) Title I districts — equity argument matches funding source. Expansion: 2026 national B2C web-launch + 10 pilot schools across 5 districts (WA/CA/TX); 2027 national + 18 paying schools with a 50-school pipeline (inside sales + NSTA/ASCD); 2028 60 paying schools + corporate partnerships (channels + state RFPs).

Industry macro trends

The post-pandemic learning loss is permanent without intervention

NAEP data shows that the 2024 8th-grade math cohort is approximately 1.5 grade levels behind the 2019 cohort. Without AI-personalized intervention, this cohort will carry the gap into the workforce. Schools are buying out of urgency; parents are buying remediation.

Teacher attrition is structural

NEA reports STEM teacher attrition at ~17% per year. With 55,000 vacancies and rising class sizes, the system structurally cannot meet K-12 STEM demand without AI augmentation. This is no longer a "nice to have" — it is infrastructure.

The CHIPS Act consequence

The CHIPS and Science Act allocated $52B to semiconductor manufacturing and $200B+ for STEM research. The unaddressed bottleneck is talent — fabs need engineers and the pipeline starts in 7th-grade algebra. Federal stakeholders are explicitly searching for K-12 interventions to fund.

Corporate workforce L&D dollars are repositioning

Companies that historically funded only their own L&D are increasingly funding K-12 STEM pipelines through corporate foundation programs. Multi-million-dollar programs exist across aerospace, defense, and semiconductor foundations — MindNova is positioned to apply as a distribution partner in 2026–2027.

The "screen time" backlash favors purposeful EdTech

Parents are increasingly hostile to attention-extraction apps (TikTok, gaming) but actively supportive of learning apps — even mandating them. Schools are banning phones in class but actively buying Chromebook-based learning software. MindNova rides the favorable side of this divide.

Regulatory tailwinds

• Title I funding — federal money explicitly allowable for AI tutoring purchases.
• State AI-in-education funding — a growing set of states fund AI-tutoring pilots and digital-learning line items directly.
• State AI-in-education frameworks — California, Virginia, Ohio publishing AI-in-classroom guidance; MindNova's compliance-first architecture is positioned to lead.
• NSF AI Institutes — direct research and pilot funding for AI-in-education partners.
• Women-in-STEM funding — NSF education-directorate programs, corporate foundations, and private philanthropy fund gender-equity STEM initiatives. A female-founded, female-focused product is strongly positioned for this pool.

Three revenue streams + grants. One platform.

Revenue streams

1. B2C Subscription (Primary, 2026–2028)

Student / parent subscription at $15/month. Freemium funnel: 3 tutor sessions per day free, unlimited at $15. Teacher referrals drive blended CAC to ~$15. This is the largest revenue line in the plan ($2.6M by 2030).

2. B2B Schools (Primary B2B, 2027–2028)

School / district subscription at $5/student/month, minimum 50 students = $250/month per school. Title I-eligible as evidence-based supplemental instruction — schools can fund it without discretionary budget. Sales cycle is slower (6–12 months) but ACVs are large and retention is high (~95% logo retention typical for school SaaS).

3. Corporate L&D (Workforce, 2027+)

$50/seat/month for enterprise workforce-bridge product. Aerospace, defense, semiconductors. Initial deals expected via inbound from companies that sponsor school pilots in their hiring geographies. Higher ACV per logo ($60K+ for a 100-seat deployment), used as a corporate workforce-development investment. Deliberately modeled small — under 7% of 5-year plan revenue — and validated through sponsored school pilots before any dedicated enterprise build.

4. Non-dilutive grants (target programs)

All grant amounts conditional on competitive award. Plan does not depend on any single grant.

Pricing strategy

Pricing is set deliberately below comparable consumer learning tools (Brilliant $24/mo; Khanmigo $4/mo is cheaper but has no simulation lab or adaptive STEM depth). Three reasons: (1) Cost structure permits it — $1.20/sub-month variable cost on $15 revenue. (2) Family affordability is the unlock — $25+ triggers a household budget conversation. (3) Land-grab dynamics — first to 100K paying students owns the category; revisit ARPU 2027+.

Customer acquisition channels

Retention & expansion

• NRR target 115% by 2028 — family-plan sibling add-ons, school full-grade upgrades, Corporate L&D up-tier.
• Retention: ~92% monthly B2C (≈12-month average subscriber life, consistent with $180 LTV) / 95% annual B2B — streak loops, parent dashboard, teacher-side daily value.
• Expansion: sibling discount lifts household ARPU; teacher leads convert to district contracts; workforce-bridge upsell to corporate L&D.

Sales motion

• Phase 1 — Founder-led (2026). Rozlana cultivates the first 150 teachers via Reddit, NSTA, direct outreach. Conversion via in-product usage-limit prompts + parent email.
• Phase 2 — Inside sales (2027). Hire 1 District Partnerships Lead. Sell into schools with >20% teacher adoption. Title I-eligible pricing.
• Phase 3 — Full engine (2028). 2 inside sales FTE plus commission-only agents; enterprise lead added 2029. Self-serve B2C + mid-market districts + enterprise L&D.

Customer success & onboarding

• Student day 0. Sign up, choose 2 subjects, 1 tutor session, first 50 XP.
• Student day 7. First streak unlock, parent progress email, first mastery badge.
• Teacher day 0. Free dashboard, roster import (Clever/ClassLink), first AI student insight within 24h.
• School day 0–90. White-glove onboarding: curriculum mapping, FERPA review, teacher PD session.

Teachers first. Then everyone.

The three-phase wedge

Why teacher-led works

• One teacher = 30 student leads. The teacher-to-student ratio is the highest-leverage channel in K-12.
• Teachers are trust intermediaries. A teacher recommendation to a parent converts at ~5× a cold parent ad.
• Teachers are the district decision precursor. Districts buy what teachers already love. Bottom-up adoption is the only path that works in K-12 SaaS.
• Free tier is cheap CAC — not free compute. At ~$0.04 per tutor session (cheap-path routing) and ~5 sessions/month for the average active free user, free-tier inference runs ~$0.20/user/month — ≈$5K in 2026 at ~40% monthly-active on 4,500 free accounts, budgeted explicitly in the marketing mix. At ~$2 effective CAC per referred paying user, it still beats every paid channel.

Channels

• Reddit (r/teachers, r/teaching) — founder-led conversation, free-tier signups. Owner: Founder.
• NSTA + ASCD + ISTE conferences — endorsement + district lead-gen. Owner: Founder + Districts Lead.
• EdSurge content + founder articles — thought-leadership. Owner: Founder.
• TeachersPayTeachers + NEA — cross-promotion to ~7M teachers + policy alignment. Owner: Growth Marketer.

Sales plays by segment

Teacher onboarding — "The 5-minute setup" play

1. Open: Reddit post / conference booth / direct DM offering "free AI lab for your STEM class."
2. Setup: Clever SSO import → roster live in 5 minutes.
3. First win: AI generates a differentiated worksheet for tomorrow's class within 24 hours.
4. Habit: Weekly mastery summary delivered Sunday evening — teacher uses it Monday morning.
5. Conversion: Teacher's students hit free-tier limits → parent email → $15 subscription.

Parent conversion — "The patient tutor" play

1. Trigger: Free-tier student hits 3-session daily cap.
2. Email: Parent receives a progress report — what their child mastered, where they got stuck, what's next.
3. Offer: 7-day free Plus trial. No card required.
4. Activation: First Plus week shows full simulation access + adaptive engine engagement.
5. Convert: $15/month, easy cancel, 70%+ retention through month 3.

District sales — "The Title I" play

1. Open: Conversation with curriculum director, opened by teacher adoption data ("12 of your teachers already use this").
2. Compliance pack: FERPA, COPPA, district-tailored DPA delivered in one PDF.
3. Pilot: One school, one grade, 60 days. Measured on engagement + mastery growth.
4. Expand: Pilot results → district-wide contract. Funded through Title I and state digital-learning programs.

Geographic rollout plan

2026 — National web launch + 10 pilot schools

National B2C reachable from day one. District pilots concentrated in WA, CA, TX where teacher adoption density is highest. Founder-led district conversations.

2027 — 18 paying schools + Series A

Add inside-sales SDR (1) and District Partnerships Lead (1). Open OR + CO + MA + VA. Series A timed to district expansion proof.

2028 — 60 paying schools + first corporate L&D contracts

Sales scales to 2 inside FTE plus commission-only agents. Workforce-bridge product launched. Corporate sponsorships fund additional school pilots.

Marketing mix & budget

Brand & content strategy

Voice: teacher-first — language of the classroom, not the SaaS conference stage. Content shows real teachers reclaiming evenings, students hitting "I get it!" moments, parents seeing confidence return. Never lead with the LLM name; never use "disrupt." Mastery, time saved, outcomes. Channels: weekly founder LinkedIn/X · YouTube "5-min simulation lab" series · TikTok/Reels student + teacher wins · monthly EdSurge guest articles · weekly teacher Substack. Owned media: mindnova.academy (SEO on "AI tutor middle school," "Khan Academy alternative") · public efficacy dashboard · free curriculum library.

Key sales & marketing milestones

Customer engagement cadence. Daily streak emails + tutor reminders · weekly parent progress digest (Sun) + teacher mastery summary (Mon) · monthly product newsletter · quarterly district business review + founder AMA.

MVP today. $3.8M revenue by Y5.

Current build status

• Platform architecture finalized — 11 modules mapped and prioritized.
• MVP in active development — target production deployment Q2 2026.
• Socratic AI tutor prototype functional in development environment (refuse-to-answer prompting design implemented).
• Three physics simulations in QA: kinematics, electrical circuits, energy conservation.
• Domain secured — mindnova.academy.
• Founder relocation to the U.S. underway.
• Curriculum partner outreach initiated.
• Technical advisor onboarded (founder of an early-stage AI ventures studio, 2% advisor equity).
• Brand identity completed — investor pitch deck delivered May 2026.

Milestones through 2028

Headline forward indicators

Research-stage signals (informal)

• Teacher pain. Founder-led informal conversations with U.S. K-12 STEM teachers (Feb–Apr 2026) consistently surfaced grading burden >10 hrs/week as the top stated time sink — corroborated by published NEA workload surveys.
• Parent willingness-to-pay. Informal pricing-sensitivity conversations with U.S. parents in target metros suggest $15/month is well within the typical household after-school spend, consistent with published Brilliant ($24/mo) consumer benchmarks and one-tenth the cost of hourly tutoring.
• Curriculum alignment. Initial NGSS + Common Core mapping completed for the first ~800 concepts. Top-10-state adoption check in progress.
• Tutor refusal-to-answer. Internal prompt-engineering experiments demonstrate high refusal rate on direct answer requests when system prompt is properly enforced.

All Research-stage signals above are pre-quantitative-study findings, not formal market research. A funded research program is in the Use of Funds (Section 13).

What we're tracking weekly

Product velocity targets

Data asset growth (Y1 → Y5). Concept graph ~1,400 → ~3,500; live simulations ~12 → ~40; tutor sessions completed ~120K → ~12M; student-mastery datapoints ~3.5M → ~380M; teacher-tagged interactions ~15K → ~2M.

We own the unowned quadrant.

Competitive landscape

Direct competitors

• Khanmigo (Khan Academy). Category leader by brand; shallow simulation, weak adaptive engine. $4/mo ($44/yr).
• Brilliant. Beautiful math & science content. Adult-focused; no K-12 workflow; no teacher-side. $24/mo.
• IXL. Massive K-12 base. Drill-and-practice, pre-AI architecture. $10–20/mo per family.
• Carnegie Learning. Adaptive math with research credibility. School-channel only; older architecture.
• Synthesis. Cohort-based; $180+/mo; doesn't scale. Adjacent category.

Indirect competitors

• Human tutors (Wyzant, Tutor.com, Varsity). $50–150/hr — out of reach for most families.
• Legacy publishers (Pearson, McGraw-Hill, Savvas). Thin digital products.
• YouTube + raw LLMs. Free but passive; no personalization, no pedagogy, no progress tracking.

Competitive positioning matrix

Why MindNova wins

1. We own the synthesis quadrant.

The two axes that matter — depth of AI personalization and depth of STEM simulation — are owned by different players (Khanmigo and Brilliant respectively). Neither has both. MindNova is the only platform built natively on the intersection.

2. We are female-founded with Women-in-STEM as product, not marketing.

Rozlana Yergaliyeva is the founder. Women in STEM is module 10 of the platform, not a press release. This positions MindNova strongly for gender-equity STEM funding from foundations and philanthropy, and for federal education programs — positioning competitors cannot bolt on credibly.

3. We are workforce-aligned.

Our curriculum maps to actual workforce skills (semiconductors, clean energy, aerospace) — which gives us a corporate L&D revenue stream and a sponsored-pilot funnel that competitors lack. Brilliant targets adults and older teens with no K-12 enterprise motion. IXL has no AI tutor architecture to underwrite a workforce partnership.

4. We are teacher-first.

The free teacher dashboard is our acquisition engine. One teacher equals 30 student leads. Khanmigo has teacher tools but they are secondary to the Khan student brand. Our entire GTM is built on teachers.

5. We are priced for adoption.

$15/month sits below Brilliant ($24/mo) and at one-tenth the cost of an hour of human tutoring; Khanmigo ($4/mo) is cheaper but offers no simulation lab or adaptive STEM depth. The price removes the conversion objection for parents and accelerates the funnel.

Moat durability

Defensibility stack. Female-founded, Women-in-STEM-native positioning · data flywheel · switching cost (mastery state doesn't port) · teacher-led distribution compounds into districts · first AI-native K-12 STEM brand with efficacy data.

Builders who ship.

Leadership

Rozlana Yergaliyeva — Founder & CEO

Visionary behind MindNova. Drives product strategy, curriculum design, and partnerships. Relocating to the U.S. full-time. Deeply committed to closing the STEM gap and empowering women in technical fields. Owns: product vision, curriculum architecture, district partnerships, fundraising, brand.

Technical Advisor — Engineering & AI Build (2% advisor equity)

Founder of an early-stage AI ventures studio; built and shipped 7+ AI products. Serves as fractional CTO through Series A — owns AI infrastructure choice and integration architecture. Day-to-day engineering is led in-house by the Senior Founding Engineer (first W-2 hire at seed close, ESOP equity), with all advisor and contractor agreements carrying IP assignment to MindNova, Inc. at incorporation. A full-time CTO search opens with the Series A. Bellevue, WA.

5-Year Personnel Plan — All U.S.-Based, Full-Time W-2 (Year 1 = 2026)

All personnel are U.S.-based W-2 employees receiving U.S. payroll, paying U.S. federal & state taxes, and counted toward U.S. job creation. Founder serves as full-time U.S.-resident CEO upon relocation. Engagements for the first six months (post-seed): one Senior Founding Engineer (Full-Stack/AI, W-2, ESOP equity) plus design and curriculum contractors and a part-time District Partnerships Lead — Bellevue HQ or U.S. remote. A Marketing & Community manager is W-2 from 2026; Head of Curriculum and full-time Districts Lead convert to W-2 hires with the Q3 2027 Series A.

Designated Salary per Position (U.S. Market Rate, Y1 → Y5)

CEO (Founder): $90K → $180K* · Engineering & AI/ML: $140K → $160K · Curriculum & Product: $95K → $108K · Sales / Districts: $85K → $98K + commission · Customer Success: $78K → $89K · Marketing & Community: $82K → $95K · Operations / Finance / G&A: $85K → $105K.

*Founder receives base salary plus net profit share from Year 3. All salaries based on 50th-percentile U.S. market data (BLS, Robert Half, Glassdoor 2025).

Total Annual Payroll Expense (U.S. Payroll)

Advisors & Board. Technical Advisor — founder of an early-stage AI ventures studio (committed, 2% equity). Education advisor and AI-safety advisor seats targeted post-seed close. Investor board seat reserved for seed lead.

Culture, cadence & compensation

Two-week sprints, daily 15-min standups (async on slow days), weekly founder + advisor sync, monthly teacher-voice review with ~6 teachers, quarterly OKRs, annual mission day. Compensation: below-market cash + meaningful equity (50th-percentile cash by Y2), 4-year vest / 1-year cliff stock options for every hire, full healthcare, $2K annual learning stipend, remote-first. Mission filter is non-negotiable — we hire only people who care deeply about the K-12 STEM gap.

Personnel summary & founder commitment

MindNova reaches 15 U.S.-based full-time W-2 employees by Year 5 (2030), growing from 3 in Year 1. U.S. payroll scales from $312K (Y1) to $1.80M (Y5) — cumulative 5-year U.S. payroll $5.20M. All positions are U.S.-based, paid through U.S. payroll, contributing federal/state/local tax revenue. Rozlana Yergaliyeva is sole founder and full-time CEO, relocating to the U.S. A Senior Founding Engineer (W-2, equity) leads day-to-day engineering from seed close with the technical advisor as fractional CTO; a full-time U.S.-based CTO is hired at Series A.

From $72K to $3.8M in revenue.

Sales Forecast (5-Year, Conservative)

Pro Forma Profit and Loss (5-Year)

NOL carryforward ($0.90M through Y3) offsets 80% of Y4–Y5 taxable income (TCJA); residual tax ≈$3K/$27K booked above. Excluding conditional grants (shown below the operating line), operating break-even arrives in Year 5.

Customer & Unit Metrics (5-Year, Conservative)

Modeling notes: MRR rows are December exit rates across all lines (B2C at $15/mo list; schools ~$600/mo average — ≈120 students, $250 minimum; corporate seats). Converted schools pay a one-time $2.5K onboarding / curriculum-mapping fee (waived inside district-wide agreements from Y4); pilots themselves are free (60 days). Recognized revenue reflects the mid-year launch (Y1) and back-to-school cohort weighting (Aug–Sep).

Cash Flow Summary (5-Year)

Quarterly pacing (pre-Series A): 2027 hiring converts to W-2 only at the Series A close, so pre-close burn holds at seed pace (~$30K/mo); the annual Y2 loss above is back-loaded into the post-close ramp. EOY-2026 cash ($140K), H1-2027 collections, and the NSF SBIR decision (Q4 2026) fund operations into Q4 2027; cost levers (hire delay, 50% marketing cut) add 4–6 months of contingency.

Pro Forma Balance Sheet (5-Year)

$500K buys the runway to Series A.

The Ask

Allocation

Milestones funded by this round

1. Q2 2026: MVP launched (500 beta users) · NSF SBIR Phase I application prepared (files upon incorporation; +$275K if awarded).
2. Q3 2026: iPad / mobile launched · 10 school pilots active.
3. Q4 2026: 500 paying subscribers · $11.5K MRR · 4 paying schools.
4. Q1 2027: FERPA / COPPA readiness complete · school pilot pipeline ≥ 20 · Series A prep.
5. Q3 2027: Series A closed (target) · Corporate L&D GA.
6. Q3 2028: $1M ARR (mid Year 3).

Earned trust, not assumed.

K-12 EdTech lives or dies on compliance. Districts will not adopt — and parents will not trust — software that handles children's data carelessly. MindNova's compliance posture is engineered, not bolted on.

FERPA (Family Educational Rights and Privacy Act)

• Student PII isolated in a dedicated schema with row-level security.
• Audit logging on every read of student data, retained for 7 years.
• Parent / eligible-student right of access enforced via in-product data-export.
• Student data never sold, ever — contractually committed in ToS and DPA.
• District-level Data Processing Agreement (DPA) template aligned to SDPC Model DPA.
• State-residency option for districts requiring in-state storage (multi-region in Year 2).

COPPA (Children's Online Privacy Protection Act, <13)

• Verifiable parental consent flow for users under 13 — email-plus consent, with school-provided consent path for B2B.
• No behavioral advertising. No third-party trackers. No data sale.
• Minimal data collection — only what is required for the educational service.
• Designated COPPA officer (Head of Curriculum until Year 2 DPO hire).

SOC 2 Type II

• Engagement begins Q4 2026.
• Type I bridge target Q2 2027.
• Type II target Q3 2027 — required for Enterprise (corporate L&D) and large-district sales.

AI Safety & Pedagogical Integrity

• Refuse-to-answer prompting. System prompt enforces Socratic posture: ask questions, offer graduated hints, never directly answer until the hint ladder is exhausted.
• Curriculum-grounded retrieval. RAG over verified curriculum corpus prevents fabricated facts.
• Mathematical correctness checks. Every numerical answer is verified through symbolic computation before being shown to the student.
• Teacher review queue. Flagged interactions (low-confidence, contested, off-topic) surface to the teacher for review.
• Red-team prompts. Weekly internal red-team runs against the tutor — jailbreaks, content-policy probes, off-curriculum drift.
• Bias audits. Quarterly audits of adaptive engine outputs across demographic groups (gender, ethnicity, ESL status) for disparate impact.
• Content policy. Off-topic refusal, age-appropriate language enforcement, no mental-health crisis response (instead, escalation to school counselor protocol).

Accessibility

• WCAG 2.2 AA compliance target at launch.
• Screen reader support for tutor dialogue and simulation lab.
• Keyboard navigation — every interaction reachable without mouse.
• Dyslexia-friendly typography as opt-in profile.
• High-contrast mode for low-vision learners.
• Closed-caption on all video content; alt-text on all images.
• ESL support — multi-language tutor responses; UI translated into Spanish at launch.

Data residency & sovereignty

• Primary U.S. region: Cloudflare + Supabase U.S.
• State-specific opt-in (TX, CA, NY) for districts requiring in-state storage by 2027.
• No international transfer of student data without explicit district consent.

Operational safeguards

• Pre-commit hooks for secrets scanning (gitleaks).
• Pen test annually starting 2027.
• Quarterly access-review of who can touch student data internally.
• Encryption: TLS 1.3 in transit, AES-256 at rest.
• Incident response runbook with 24-hour disclosure target for material breaches.
• Cyber-insurance and EdTech-specific E&O coverage in 2027.

State-level frameworks we comply with

What could go wrong, and what we do about it.

Strategic risks

R1. Khanmigo bundles deep simulations

Likelihood: Medium. Impact: Medium.
Mitigation: 12–24 month head-start on simulation depth and adaptive engine. Teacher-first GTM creates switching cost. Female-founder + Women-in-STEM positioning is a structural moat Khanmigo cannot replicate on demand. We can also pivot to be the "Khanmigo alternative for STEM teachers" if needed.

R2. School sales cycle is slower than planned

Likelihood: High. Impact: Medium.
Mitigation: B2C revenue carries the plan in 2026; B2B is the 2027–2028 lever. Teacher-led growth creates inbound that compresses cycles. Pilot offering is no-cost and short (60 days), removing procurement friction.

R3. Parent willingness-to-pay weaker than survey indicates

Likelihood: Medium. Impact: Medium.
Mitigation: $15 price point is below most consumer alternatives. Free tier seeds trust. School channel exists as fallback if B2C struggles. Sensitivity model shows the plan survives a 30% B2C slowdown.

Technical risks

R4. LLM provider price increase or outage

Likelihood: Medium. Impact: Medium.
Mitigation: Multi-provider gateway (OpenAI, Anthropic, Google) with auto-failover. Cost increases pass through to gross margin but do not destroy unit economics — at 3× current LLM prices, variable LLM cost rises from ~$1.20 to ~$3.60 per sub-month (total COGS ~$2.70 → ~$5.10) and gross margin compresses from 82% to ~66% — still workable.

R5. AI hallucinates a wrong math answer

Likelihood: Low (mitigations are strong). Impact: High (single bad screenshot can damage trust).
Mitigation: Refuse-to-answer prompting (we mostly don't generate direct numerical answers). Curriculum-grounded RAG. Mathematical correctness checks via symbolic computation. Teacher review queue. Public efficacy dashboard.

R6. Data breach

Likelihood: Low. Impact: Very High (FERPA breach = district contract termination).
Mitigation: Compliance-first architecture from Day 0. SOC 2 by Q3 2027. Pen tests annually from 2027. Cyber-insurance from 2027. Incident-response runbook with 24-hour disclosure target.

Market & execution risks

R7. Teacher acquisition stalls

Likelihood: Medium. Impact: High (teacher-led is the core GTM).
Mitigation: Multi-channel teacher acquisition (Reddit, NSTA, ASCD, NEA, TeachersPayTeachers). Founder personally cultivates the first 150 teachers. Diversification into direct-parent paid acquisition as a back-up funnel.

R8. Founder bandwidth / key-person risk

Likelihood: Medium. Impact: High.
Mitigation: Technical advisor leads engineering through MVP and Series A. Head of Curriculum hire offloads curriculum execution. District Partnerships Lead offloads sales execution. Full-time CTO targeted post-Series A.

R9. Founder relocation timing

Likelihood: Low–Medium. Impact: Low (impacts U.S. closing logistics, not company operations).
Mitigation: Experienced counsel engaged. Founder can direct early-stage operations remotely while relocation completes; the U.S.-based advisor, contractors, and conference calendar keep in-person district work moving in the interim.

R10. State regulatory shifts on AI in classrooms

Likelihood: High (this is happening). Impact: Low–Medium (we are positioned to lead, not lag).
Mitigation: Compliance-first architecture means most new state frameworks are tailwinds, not headwinds. Active monitoring of CA, VA, OH, IL AI-in-education guidance.

Financial risks

R11. Slower revenue ramp than modeled

Likelihood: Medium. Impact: High.
Mitigation: Plan tolerates a 30% B2C slowdown without bridge. Non-dilutive grant stack ($550K cumulative through 2028) provides redundancy. Aggressive cost-cut levers (delay hires 1–2 quarters, reduce marketing 50%) preserve runway.

R12. Series A delay

Likelihood: Low–Medium. Impact: Medium.
Mitigation: Lean pre-close burn (~$30–35K/mo steady state after the 2026 build phase) plus the non-dilutive grant stack extends runway beyond the target close; cost-cut levers (delay hires 1–2 quarters, cut marketing 50%) add 4–6 months more. Seed milestones (500 paying users, 10 pilots) are sized to be hit well before cash-out, keeping the round competitive.

Exit pathways.

Path 1 — Strategic Acquisition (Most Likely)

EdTech consolidation is steady and AI-native is becoming a board-level priority. Three buyer classes are realistic:

• Legacy K-12 publishers (Pearson, McGraw-Hill, Savvas, Houghton Mifflin Harcourt) — need an AI-native STEM product. Outcome range: $50–150M.
• Adjacent EdTech consolidators (Renaissance Learning, IXL, Curriculum Associates, Imagine Learning) — fit the AI-tutor + simulation hole. Outcome range: $40–100M.
• Tech platforms with K-12 ambitions (Google for Education, Microsoft Education, Apple) — strategic acquirers or distribution partners. Outcome range: $80–200M+.

Path 2 — IPO (Lower Probability, Higher Outcome)

If MindNova reaches $50M+ ARR with NRR >120%, defensible data moat, and demonstrated efficacy, IPO is viable 2030–2032. Comparable: Duolingo IPO'd in July 2021 and traded to a ~$6.5B market cap within months.

Path 3 — Sustained Cash-Flow Business

At 82–84% gross margin, MindNova can operate as a cash-flow positive private company indefinitely from 2029 onward. Founder retains control and optionality.

Comparable transactions

Illustrative return profile

The next 36 months.

12-month roadmap (Q2 2026 – Q1 2027)

24-month roadmap (Q2 2027 – Q1 2028)

36-month vision

Category leadership — default AI-native STEM platform in U.S. middle/high schools. Workforce pipeline — 200K+ MindNova-credentialed students annually with career outcomes tied to mastery records. Corporate — 8+ Fortune-500 L&D contracts (aerospace/defense/semiconductor/clean-energy). Federal — NSF Phase II awarded, DOE workforce partner, named in CHIPS-adjacent K-12 initiatives. International — Canada launch + Europe exploratory (UK, NL, IE). Open standards — public efficacy dataset + Socratic-tutor benchmark.

North Star Metric

Concept-mastery hours per active student per week — the single metric that captures product use, learning value, and revenue together.

Reference materials.

A. Glossary

B. Key references

• U.S. Bureau of Labor Statistics — STEM Occupations Projections (2024)
• NAEP — National Assessment of Educational Progress, 2024 mathematics
• NEA — Educator Workforce Report (2025)
• NSF — Women, Minorities, and Persons with Disabilities in S&E (2025)
• HolonIQ — Global EdTech Market Outlook (2025)
• Owl Ventures + Reach Capital — K-12 EdTech Investment Reports (2024–2025)
• Tutor.com + Wyzant — Published pricing pages (2025)
• U.S. Department of Education — Title I funding allocation tables (2024)

C. Data room

Available to qualified investors under NDA. Contents include: cap table (current + post-seed), financial model (Excel, monthly granularity), customer research (teacher and parent interviews), curriculum architecture, technical diagrams, compliance posture, product demos, founder + advisor bios, legal docs (incorporation, IP assignments, employment templates), trademark filings.

D. Contact

E. Forward-looking statements

This document contains forward-looking statements regarding MindNova, its business, products, market opportunity, financial projections, and strategic plans. These statements are based on management's current expectations, estimates, and assumptions, and are subject to significant risks and uncertainties. Actual results may differ materially from those projected. Factors that could cause such differences include, but are not limited to: K-12 adoption rates of AI tutoring tools, competitive dynamics from Khanmigo and other incumbents, regulatory changes (FERPA, COPPA, state-level AI-in-education frameworks), technological challenges (LLM provider pricing, model behavior), retention of the founder and key personnel, ability to raise additional capital on favorable terms, macroeconomic conditions affecting school district budgets, and the inherent uncertainty of pre-revenue projections. This document is not an offer to sell or a solicitation of an offer to buy any securities. Any offer of securities will be made only pursuant to definitive transaction documents in compliance with applicable U.S. securities laws.

F. Confidentiality notice

This document is confidential and proprietary to MindNova. It is being provided to the recipient solely for the purpose of evaluating a potential investment in the Company. The recipient agrees to maintain the confidentiality of this document and its contents, to use it solely for the stated purpose, and not to disclose, distribute, or reproduce any portion without prior written consent of the Company. Upon request, the recipient agrees to return or destroy all copies. Any unauthorized use or disclosure may result in legal action.