Assumption map
Researched April 2026 · statuses updated July 2026.
None of these assumptions has been validated with users yet — the friends-and-family build is how they get tested. What has changed since April is that the architecture answered some by design (retention, connection friction, LLM trust), while the current no-fee track defers others entirely (willingness to pay, every GTM assumption). One gap the original map lacks: everything here is US-framed, and India — a full jurisdiction in the current build — has no researched assumptions at all. Each of the top-5 risks below carries its current status.
Value Assumptions
| # | Assumption | Risk | Confidence | Validation Method |
|---|---|---|---|---|
| V1 | The core job is "I don't know if I'm doing it right" — a confidence gap, not an optimization gap. Users want reassurance and clarity more than they want to squeeze out 50bps of alpha. | HIGH | Medium | User interviews (10-15 target users). Ask what they'd do differently if they had a trusted advisor — listen for "tell me I'm OK" vs. "maximize my returns." |
| V2 | The target user (25-35, $500K-$5M) has sufficient portfolio complexity to need more than a robo-advisor. Specifically: 4+ accounts across 2+ institutions with at least one complex asset type (RSUs, backdoor Roth, etc.). | Medium | Medium-High | Survey of target demo. Quantify account count, institution count, asset types. Research suggests ~35% cluster in "high complexity" — validate this. |
| V3 | A cross-account X-ray showing what they actually have — with analysis of what's suboptimal — delivers immediate, felt value. The "aha moment" is seeing the full picture for the first time. | HIGH | Medium | Prototype test. Build a static mockup with a real user's data (anonymized). Show it to them. Measure reaction: relief? surprise? indifference? |
| V4 | Users care about tax optimization enough to pay for it. TLH flagging, asset location analysis, and fee drag quantification are compelling value props — not just nice-to-have analytics. | Medium | Medium | User interviews. Ask: "If I told you you're leaving $X/year on the table in unnecessary taxes, what would you do?" Test with specific dollar amounts. |
| V5 | The anxiety is real and persistent, not fleeting. Users don't just worry during market drops or tax season — there's a background hum of "I should figure this out" that sustains ongoing engagement. | Medium | Medium-High | Behavioral evidence from community analysis (r/fatFIRE, Blind). User interviews — ask about frequency of checking accounts, Googling financial questions, talking to friends about money. |
| V6 | RSU/equity comp complexity is a strong enough wedge to anchor the beachhead. Tech workers with RSUs have recurring, time-sensitive financial decisions that generic tools can't help with. | Medium | High | Already well-evidenced from community signals. Validate with 5-8 interviews with public-company tech workers. Ask about their RSU sell strategy and confidence level. |
Usability Assumptions
| # | Assumption | Risk | Confidence | Validation Method |
|---|---|---|---|---|
| U1 | Users will connect all their accounts (4-7 across multiple institutions). The value depends on a complete picture, but each additional account connection is friction. | HIGH | Low-Medium | Onboarding funnel analysis from comparable apps (Empower, Monarch). Prototype test — measure drop-off per account added. What % connect 4+? |
| U2 | CSV/PDF upload gives accurate enough data for the X-ray to feel trustworthy. With a CSV-first approach, data quality risk is much lower (brokerage exports are source of truth). Remaining risk: can we parse the top 5 brokerage CSV formats reliably? | Medium | Medium-High | Technical spike: collect sample CSV exports from Fidelity, Schwab, Vanguard, E*Trade, IBKR. Build parsers. Test edge cases (multiple accounts, transferred positions, options). |
| U3 | LLM-powered explanations increase trust rather than undermining it. The target demo is sophisticated enough to be skeptical of AI. "AI financial advisor" could trigger eye-rolls. | Medium | Medium | A/B test: same recommendations with (a) structured text explanation vs. (b) conversational LLM explanation. Measure perceived trustworthiness and action rate. |
| U4 | Manual data entry for RSU schedules, cost basis corrections, and poorly-connected 401ks is acceptable friction for the value delivered. | Medium | Low-Medium | Prototype test. Ask users to enter RSU vesting schedule manually. Measure completion rate and frustration. Consider CSV upload as alternative. |
Viability Assumptions
| # | Assumption | Risk | Confidence | Validation Method |
|---|---|---|---|---|
| B1 | Users will pay $10-$30/month for ongoing portfolio insight and optimization guidance. For the MVP (X-ray + analysis + TLH flagging), pricing should be comparable to premium finance apps ($15/month range), not full advisor replacement. Higher pricing ($50-$150/month) applies later when the full playbook is built. | Medium | Medium | Price sensitivity testing. Show the product concept with different price points to 20-30 target users. Van Westendorp or Gabor-Granger method. Also test: would they pay $X/month if it saved them $Y/year? |
| B2 | Retention is sustainable beyond the initial X-ray. Users don't just look once and leave — they come back for ongoing monitoring, new TLH opportunities, rebalancing alerts, and updated analysis. | HIGH | Low | Hardest to validate pre-product. Proxy: survey how often target users currently check their portfolio. Engagement data from comparable tools (Empower dashboard, Monarch). Design for re-engagement triggers (proactive alerts, periodic reports). |
| B3 | Unit economics work at $10-30/month with a CSV-first approach. Without aggregator costs, the primary costs are infrastructure and market data — much lighter. Aggregator costs become a concern only if/when real-time sync is added later. | Low | High | Model unit economics. With CSV-first, per-user marginal cost is near zero. The question shifts to: can you acquire users cheaply enough? |
| B4 | The business can grow without needing to become a full robo-advisor (AUM-based) to be viable. Subscription revenue on advisory-only is sufficient. | Medium | Medium | Depends on B1 and B2. If retention is strong and pricing holds, subscription works. If not, the pull toward AUM-based revenue may be hard to resist, fundamentally changing the product. |
Feasibility Assumptions
| # | Assumption | Risk | Confidence | Validation Method |
|---|---|---|---|---|
| F1 | RIA registration is achievable without prohibitive cost or delay. Budget estimate: $50-150K/year. Timeline: 1-4 months for registration. | Low | High | Known path. Competitors have done it. Consult securities attorney — budget $5-10K for initial assessment. File early. |
| F2 | Cross-account portfolio analysis and asset location optimization can be built to advisor-grade quality by a small team in 6-9 months. The algorithms exist in academic literature. | Medium | Medium-High | Technical spike: build the asset location optimizer against 3-5 model portfolios. Validate output with a CFP. Complexity is in edge cases, not the core algorithm. |
| F3 | LLM hallucination risk in financial explanations can be adequately controlled. RAG over curated financial content + citations + confidence calibration keeps the AI from fabricating tax rules or misquoting IRS thresholds. | Medium | Medium | Build a test harness: 100 common financial questions, run through the LLM pipeline, have a CFP grade accuracy. Target: under 1% material error rate. |
| F4 | Position-level TLH flagging (without lot-level cost basis) is still valuable and defensible. Users accept "you have approximately $X in harvestable losses" with appropriate caveats, rather than exact lot-level instructions. | Medium | Medium | User interviews. Present a mock TLH flag with and without lot-level precision. Does the approximate version still feel useful? Or does imprecision undermine trust? |
GTM Assumptions
| # | Assumption | Risk | Confidence | Validation Method |
|---|---|---|---|---|
| G1 | Tech workers at public companies are reachable through Blind, Reddit (r/fatFIRE, r/cscareerquestions), Twitter/X, and Hacker News at a viable CAC. | Medium | Medium-High | Run a small content marketing test. Post useful financial analysis content (anonymized portfolio X-rays, TLH case studies) to these channels. Measure engagement and inbound interest. Budget: $0 + time. |
| G2 | The product spreads virally within companies. If one engineer at Google uses it, they tell 5 others because everyone has the same equity comp structure and 401k. | Medium | Medium | Hard to validate pre-product. Proxy: ask target users in interviews "if this worked for you, would you tell coworkers? Why or why not?" Design referral mechanics early (anonymized company benchmarks, shareable insights). |
| G3 | "Everything your advisor does, without the $20K fee" resonates as positioning and doesn't trigger skepticism. The target demo already suspects advisory is commoditized. | Medium | Medium | Landing page test. Run two positioning variants to target demo: (a) "your AI wealth advisor" vs. (b) "the portfolio optimization your advisor charges $20K/year for." Measure click-through and signup intent. |
| G4 | Tax season (Jan-Apr) and RSU vesting cycles (quarterly) are strong enough acquisition triggers to drive seasonal spikes. | Low | High | Well-evidenced from financial app install data. Time marketing pushes to these windows. Validate with first cohort. |
Team Assumptions
| # | Assumption | Risk | Confidence | Validation Method |
|---|---|---|---|---|
| T1 | A team of 3 (2 eng + 1 product) plus part-time compliance/financial consultants can ship a credible MVP in 4-6 months. | Medium | Medium | Depends on founding team's domain expertise. If founders have fintech experience, timeline is realistic. If not, add 2-3 months for learning curve on aggregator integration, financial data normalization, and regulatory nuance. |
| T2 | The founding team can navigate RIA compliance without a full-time compliance officer for the first 12-18 months. Part-time consultant + compliance-as-a-service (RIA in a Box, etc.) is sufficient. | Low | High | Known path. Many early-stage RIAs operate this way. Validate with compliance consultant during initial legal assessment. |
Top 5 Riskiest Unvalidated Assumptions
These are the assumptions that are both high-risk and unvalidated — the ones that could kill the idea if wrong:
1. B2: Retention beyond the initial X-ray
Why it's #1: If users look at their portfolio X-ray once, say "interesting," and never come back, the business doesn't work at any price point. The X-ray is a one-time insight. Ongoing value requires proactive alerts, new TLH opportunities, rebalancing triggers, and periodic re-analysis that makes users return weekly/monthly. What to watch: Engagement patterns from comparable dashboards. Empower's free tools have ~2% conversion to paid — what does the other 98% do? Do they engage weekly or abandon? Status (July 2026): still the #1 open question, but no longer undesigned. The one-and-done X-ray this entry worried about isn't what got built: nightly re-pricing makes the portfolio fresh every day, and the daily briefing → structured actions → verification loop is a designed reason to return. Whether it works is exactly what the friends-and-family cohort tests.
2. V3: Cross-account X-ray delivers immediate, felt value
Why it's #2: The hypothesis is that seeing the full picture for the first time is a powerful "aha moment." But it might not be. Users who've been managing their money ad-hoc for years may look at the X-ray and think "yeah, I know it's messy" rather than "wow, I didn't realize." The specific insights (overlap, fee drag, asset location mistakes) must land as genuinely new information. What to watch: Show target users an X-ray of their actual portfolio. Do they learn something they didn't know? Is their reaction "huh, interesting" or "oh shit, I need to fix this"? Status (July 2026): subsumed rather than resolved. The one-time X-ray became a recurring surface — drift, exposure, fees, and concentration computed continuously in the fact catalog — but whether the first look lands as an aha still needs real users.
3. B1: Willingness to pay $10-$30/month
Why it's on the list: At $10-$30/month the pricing risk is comparable to Monarch and Copilot territory, and unit economics are healthy. The question is purely demand-side: does the target demo perceive enough ongoing value to pay for portfolio analysis they could theoretically DIY? What to watch: Price sensitivity testing. Landing page conversion at different price points. Whether the expansion path (full playbook → $50-$150/month) is believable once the core value is proven. Status (July 2026): deferred — no fees at the friends-and-family level. Meanwhile the market moved in this assumption's favor: Mezzi repriced to $299–$1,499/year, evidence that the category supports more than $15/month.
4. U1: Users will connect all their accounts
Why it's #4: The product's core value — cross-account analysis — requires data from 4-7 accounts, and every connection is friction. What to watch: Completion rates per account added. Can the product deliver meaningful value with partial data? If the analysis shows enough value on 2 accounts, users may be motivated to add the rest. Status (July 2026): friction largely mooted by the rail decisions — SnapTrade OAuth for the US and the no-login, all-accounts CAS statement for India replace per-account CSV wrangling; CSV remains only for the long tail. The residual question is trust, not effort.
5. G1/G2: Can we reach the beachhead cheaply and does it spread?
Why it's #5: The product economics depend on low CAC. The hypothesis is that tech workers are reachable through Blind/Reddit/HN and the product spreads within companies organically. If acquisition costs are $50-$100+ per user at subscription price points, the math doesn't work. What to watch: Content marketing test pre-launch. Engagement on target channels. Referral mechanics. Status (July 2026): deferred with everything GTM — resumes when Felix moves past friends-and-family.
Assumptions NOT on the risk list (and why)
- Data quality (U2): CSV-first and brokerage-native SnapTrade data largely solve this. Brokerage records are the source of truth. Parsing reliability is a technical task, not a strategic risk.
- Unit economics (B3): Confirmed by the build — roughly $85–95/month total deployment cost, ~$20/month marginal per user (see the cost model).
- Regulatory (F1): RIA registration is a known, solvable problem — and parked entirely while Felix charges nothing.
- Technical algorithms (F2): Portfolio analysis, TLH identification, and asset location are well-studied; the fact catalog builds them as tested, deterministic code.
- LLM trust (U3): Partially answered by design — every recommendation carries "based on" evidence chips citing deterministic facts, the "explainable reasoning" the competitive research identified as the #1 trust builder. Still needs user validation.
- Beachhead selection (V6): Well-evidenced, but dormant — the beachhead is a GTM decision now, not a build input.
- GTM channels (G4): Seasonal triggers (tax season, RSU vesting) are well-supported. Not zero risk, but not top-5.
- Team (T1, T2): Standard execution risk. Not specific enough to this idea to be a top concern.