CoinQuest01 The problem, from first principles
Start at the root. Financial illiteracy is not a content shortage — there are thousands of free lessons on budgeting. It persists for two structural reasons. First, exposure is not mastery: a student can sit through a personal-finance unit and still be unable to build a budget, because watching and doing are different cognitive acts. Second — and this is the deeper one — almost no one measures it. Schools can tell you a student took the course; very few can tell you whether the student can actually do the thing. You cannot manage what you cannot measure, and the entire category is flying blind at the student level.
Thirty states now mandate personal finance (NEFE, 2025), and the number climbs every year. But a mandate produces a course requirement, not competence. The mandates have created demand for something that barely exists yet: a curriculum that builds mastery and can prove it. That void is the opening.
02 What CoinQuest is, at root
CoinQuest is not a curriculum with a coat of game paint. It is an instrumented learning system — software that teaches a concept, has the student apply it immediately, and turns every interaction into a transparent, rules-based signal of how well that student is mastering it.
03 The product, by its four layers
The cleanest way to understand the system is the four-layer breakdown — data, structure, style, behavior. Each layer does a distinct job, and the value compounds from the bottom up.
The heart of CoinQuest, and the thing competitors don't have. Every response is timed and scored into a transparent, rules-based mastery estimate from 0 to 100 — deliberately conservative: earned from zero, asymmetric (harder to gain than to lose), and it decays if unused. We classify each error by response time — a fast miss reads as a careless slip, a slow one as a misconception or conceptual gap — a signal teachers act on, judged relative to that student's own pace, per grade band. And we're honest about what it is: every point it moves traces to a rule you can inspect, not a claim to have measured "true understanding." That auditability — interpretable by design — is what makes the number trustworthy to a district, exactly what you want in a system that scores children.
Six immersive worlds, one per grade band, kindergarten through twelfth. Seventeen financial domains — from coins and saving up to taxes, credit, and investing. 184 modules, each mapped to a Bloom's depth level and to the frameworks districts require — Common Core math, NGPF, the Council for Economic Education, and Jump$tart. Progression is prerequisite-aware: the engine never tests a concept before its foundations unlock. And the data model is built so the district is the FERPA controller. The structure is what makes CoinQuest both pedagogically sound and adoptable at scale.
A child meets a guide who grows up with them — warm Copper on Penny Island in the early grades, all the way to Morgan in the Executive Suite for seniors — inside worlds they actually want to explore. For the adults, the same telemetry surfaces as something usable: a teacher's live heat map of who needs help, a parent's weekly progress view, a district's analytics. Style is the adoption wedge: kids engage, so teachers adopt; teachers see results, so districts buy.
The engine is CALE — our Competitive Adaptive Learning Engine. It schedules reviews just before a concept would fade, scores asymmetrically so a lucky streak can't fake mastery, gates on prerequisites, and delivers a teaching moment when a student stumbles — pausing to re-teach, then re-testing. No two students get the same path. This adaptive behavior is the moat: a fixed-content tool, however polished, structurally cannot do it. And the moat is protected in a way most startups can't claim: CALE is patent-pending (provisional Serial 64/007,323), and the engine we ship provably implements the filed claims — its scoring and error-taxonomy are checked against the patent by an automated conformance gate that blocks any release that drifts. The IP isn't a filing in a drawer; it's the running product.
Read those four layers from the bottom up and you have the whole company: data is the moat, structure makes it adoptable, style drives adoption, and behavior is what no free competitor can copy.
04 Why now
Two curves are crossing. The mandate wave is real — thirty states today, and once rollouts complete, roughly 73% of high-school students will receive financial education before graduating — up from just 9% in 2017 (NEFE, 2025). At the same time, the measurement void is wide open: the incumbents prove exposure, not mastery, and districts are increasingly required to spend against evidence. A measurable, standards-aligned, adoptable product walking into that moment is the definition of timing.
05 Competition, honestly
Districts already run free, entrenched tools — EVERFI and NGPF — and engaging consumer apps like Zogo and Banzai. I won't pretend they don't exist. The incumbents are gold-standard curriculum, but largely fixed and exposure-based; the apps are motivating but thin on mastery tracking and district reporting.
We win exactly on the two layers free tools can't reach: Data — per-student rules-based mastery measurement and error diagnosis — and Behavior — real adaptivity. Where we don't win yet, and I'll say it plainly: they're free and entrenched, and we are pre-efficacy-study. Closing that evidence gap is the single most important thing this raise funds.
06 The economics, from first principles
Build it up from the atom — one paid student-seat. The published list price is $7–15 per student/year, tiered by district size, alongside a free individual tier and Teacher Pro at $8/month. The marginal cost of one more student is almost nothing — content is generated once and amortized — so the gross margin is around 80%, true SaaS economics. A free-teacher adoption funnel keeps customer-acquisition cost low, targeting CAC payback near the ~12-month mark institutional investors look for, with a healthy LTV-to-CAC. The serviceable market across the 30 mandate states is on the order of $90–125 million, inside a $360M+ national TAM (NCES enrollment × published pricing).
07 The team and the raise
Lean by design. I build the product, the engine, and the roadmap. This raise adds one system steward — reliability, quality, and keeping the IP confidential under a real NDA and full IP assignment — not a second builder. One person builds, one guards.
We're raising a Phase 1 angel round (of a phased Angel → RBF → institutional path) to fund three things: the engineering hire above, a third-party efficacy study (a real ESSA-tier signal, scaling to a powered study after the raise), and district go-to-market in the mandate states. Round size, instrument, and the full financial model are shared in conversation — jamal@reaveslabs.ai. And I'll be direct about scope: at the lean end of the range we go depth-first on the mandate-bearing 7–12 grade bands; the full elementary build-out and new worlds ride the next round. The 5–6 demo already ships as the proof.
Crucially, this is not a greenfield bet: the product is live today — the CALE engine in production, 184 modules shipping, Clever and Google SSO and the dashboards working. The raise completes, hardens, and proves a working system.