WealthCreate
Open prototype

00 · Executive Summary

WealthCreate: the platform that teaches people to invest, then helps them do it

A CTO proposal for the founding team. It covers the market, the customer journey, a phased roadmap, the platform architecture and content direction, and it comes with a working prototype you can use today.

Prepared by Chris Therriault · ServiceVision11 November 20268 documents + working prototypeEvery statistic cited

Why now

The opportunity in one paragraph

Americans are no better at money than they were in 2017: adults answer 49% of basic personal-finance questions correctly, Gen Z only 38%, and risk is the weakest topic at 36%.[2] Meanwhile the pipeline of new investors has collapsed. Only 8% of investors started in the last two years, down from 21% in 2021, and just 21% of adults under 35 hold any investments.[1] The people who are trying are learning from the wrong teachers: 61% of under-35 investors follow finfluencer recommendations.[1] AI is becoming the next advice channel, but only 3% of adults trust it “a great deal” with money.[3] Yet 70% of investors say they would invest more if they had more chances to learn.[4] That gap is the business: a credible, transparent, education-first platform that turns learning into a first investment and a lasting habit.

49%

Financial literacy score, U.S. adults

Unchanged since 2017. Gen Z 38%. Risk questions 36%.

Source

8%

Investors who started in the last 2 years

Down from 21% in 2021. New-investor inflow has collapsed.

Source

61%

Under-35 investors who follow finfluencers

57% among those with under 2 years of experience.

Source

3%

Adults who trust AI with money “a great deal”

Yet ~25% of Gen Z and millennials already ask AI.

Source

Point of view

Our thesis

WealthCreate should be the anti-finfluencer. Not another brokerage with a blog, and not a chatbot that tells you what to buy. It is a learning product first, with an honest AI tutor, a practice environment, and a clean bridge to real investing once the learner is ready. Every design choice follows from one loop:

01Learn

Short, sequenced lessons with mastery tracking, delivered at the moment of a real decision.

02Practise

Paper portfolios that let people live through a drawdown before they risk real money.

03Invest

A one-tap default (auto-invest, auto-escalate) through a regulated broker partner from Phase 2.

04Grow

Goal progress, spaced review and the next concept, so learning keeps pace with the money.

Why the loop works

  • Financial education does change behaviour. A meta-analysis of 76 randomised trials with 160k+ people found real effects on saving and budgeting.[7]
  • Those effects fade to near zero within about 20 months unless they are tied to a decision and reinforced.[8] Hence just-in-time lessons and spaced review.
  • An AI tutor told not to give direct answers produced more than double the learning gains of an active-learning class.[9] Our coach teaches; it does not pick stocks.
  • Defaults move behaviour more than anything else: auto-enrollment took 401(k) participation from 37% to 86%.[19] Every module ends in a one-tap default.

Education-first compliance posture

  • Phase 1, education only. Impersonal teaching stays inside the publisher's exclusion (Lowe v. SEC).[11] No security-specific advice tied to a user's own situation.
  • Phase 2, embedded investing. Real accounts through a registered broker-dealer partner (Alpaca is the default) that runs KYC and custody.[16]
  • Phase 3, advice. Only once the data and the compliance team justify RIA registration or a sub-advisory arrangement.
  • Every coach conversation is logged and archived from day one. FINRA now treats AI chatbot output as a firm communication.[12]
  • Gamify learning and saving, never trading. Robinhood paid $7.5M to Massachusetts over confetti and trading nudges.[10]

Navigate

What's in this proposal

Each document stands alone and takes 5–10 minutes to read. If you only read two, read Market (01) and Engagement (07).

Try it

The working prototype: a 10-minute guided tour

A proposal can describe a product; a prototype lets you argue with one. Everything below runs in your browser with demo data. Follow the steps in order; the times are approximate.

  1. 1

    Pick the Maya Okafor persona (24, ICU nurse, first-time investor who has been frozen by fear of getting it wrong). No real credentials are needed.

  2. 2

    Five quick questions shape the learning path. Notice what it never asks for: no SSN, no account numbers. The profile decides what you learn next, never which product you should buy.

  3. 3

    One next step, not twenty. Progress is measured in concepts mastered and habits formed, not in portfolio returns.

  4. 4

    A short, interactive lesson that ends in a decision a learner can actually act on. This is the just-in-time pattern the research supports.

  5. 5

    The anti-finfluencer module: learners practise identifying hype, guaranteed-return claims and undisclosed conflicts.

  6. 6

    Ask it something like “should I put my $5k into one hot stock?” It will teach instead of recommend, cite the lesson it drew from, and stay inside the education-only line.

  7. 7

    A paper portfolio with a diversification score and behavioural nudges. There is no confetti and no reward for trading more often.

  8. 8

    See the cost of waiting and a range of outcomes as probability bands rather than a single promised number.

  9. 9

    Sign in as Ana Lindqvist, Head of Learning, to see the AI-assisted content pipeline, the compliance review queue and cohort analytics.

Bottom line

Key recommendations

  1. Position as the credible, education-first alternative to finfluencers, not as “AI that picks stocks”. The trust gap (3% strong trust in AI) is the opening.[3]
  2. Launch education-only in the U.S. first. Get a securities counsel memo on the publisher's exclusion before the coach goes live, then sequence UK and EU with counsel at each gate.
  3. Build the coach as a Socratic tutor with guardrails: input classifier, retrieval over vetted lessons with mandatory citations, output checks for buy/sell language, and an eval suite of 300–500 prompts that runs on every change.
  4. Simulate risk, not stock-picking. Let learners live through a 30% drawdown on paper before they commit money. Never reward trading frequency.[10]
  5. Use Alpaca as the default investing rail. The same API covers Phase 1 paper trading and Phase 2 real accounts, and it has an EU entity.[16]
  6. Price at Free / Plus $8 a month or $72 a year, and sell annual by default. Robinhood converts 14%+ of funded customers at $5 a month,[5] Cleo's subscription revenue works out to about $69 per payer per year,[6] and annual plans keep roughly half their subscribers at six months versus 12–22.5% for monthly.[18]
  7. Run one B2B2C pilot in parallel as the acquisition hedge. 92% of employers prioritise financial health but only 28–29% offer education beyond retirement,[14] and Zogo shows 240+ financial institutions will license this kind of product.[15]
  8. Use Trump Accounts as a timely wedge. They went live on July 4, 2026 with a $1,000 federal seed for children born 2025–2028,[13] creating millions of new parents who need clear explanations. A family track plus search content is low-competition and on-mission.

The candidate

Why me as your CTO

You asked for someone who has built and launched platforms from scratch, has a real AI and LLM background, and can code. Here is the evidence, not the adjectives.

I build and ship, end to end

Through ServiceVision I design, build and run production platforms from first commit to live customers: payment APIs, multi-tenant commerce, marketplace platforms, and AI products. Architecture, deployment pipelines, data models, security reviews and the on-call that follows launch.

I code every day

I am a hands-on CTO, not a manager of contractors. I would write a substantial share of the MVP myself while hiring a founding engineer, and every architectural decision would be written down as an ADR so the codebase never depends on one person.

I run LLMs in production

I build with model routing (small/fast and large/reasoning model tiers, e.g., the Anthropic Claude family), retrieval with citations, guardrail classifiers, eval suites in CI, prompt caching and per-user cost controls. The research behind this proposal puts a guardrailed coaching conversation at about $0.035–0.04 on list prices.[17]

This proposal is the proof

The hub you are reading and the working prototype behind it were researched, designed and built in days. That is the speed I would bring to your MVP, with the compliance and engineering discipline described in the documents that follow.

AI was used to accelerate research and drafting; every figure links to its primary or reported source.

Sources

  1. [1]FINRA Foundation — NFCS Investor Survey (Dec 2025)
  2. [2]TIAA Institute–GFLEC Personal Finance Index 2025
  3. [3]Fortune / Gallup — AI usage vs. trust in financial advice (Aug 2026)
  4. [4]FA Magazine — 70% would invest more with more chances to learn
  5. [5]Robinhood Q3 2025 results (Gold subscribers, attach rate)
  6. [6]Sacra — Cleo revenue and subscription mix
  7. [7]Kaiser, Lusardi, Menkhoff & Urban (2022), Journal of Financial Economics — 76 RCTs
  8. [8]Fernandes, Lynch & Netemeyer (2014), Management Science — decay of effects
  9. [9]Kestin et al. (2025), Scientific Reports — AI tutor RCT
  10. [10]WealthManagement.com — Robinhood $7.5M Massachusetts gamification settlement
  11. [11]Lowe v. SEC, 472 U.S. 181 (1985) — publisher's exclusion
  12. [12]Troutman — FINRA 2026 Oversight Report (GenAI section)
  13. [13]Forbes — Trump Accounts launch (July 4, 2026)
  14. [14]Business Group Health — 2025 Employer Well-being Strategy Survey
  15. [15]Next City — Zogo licensed to 240+ financial institutions
  16. [16]Alpaca Broker API documentation
  17. [17]Anthropic API pricing (model tiers, caching, batch)
  18. [18]RevenueCat — State of Subscription Apps 2025
  19. [19]Thaler & Benartzi — Save More Tomorrow / auto-enrollment evidence

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