What do you think your score would be? And would you want to know it?
Based on fees, investment selection, and time horizon.
I guess what I’m asking is, do you feel confident that you’re on track? And if you don’t know exactly what you’re paying in fees, your score would likely be a lot lower than you think. Unfortunately….
I spent 10+ years as a financial advisor managing ~$100M, and one gap kept coming up over and over:
People don’t know if they’re actually on track for retirement.
Not balances. Not returns.
Just a simple answer to: am I doing this right?
There’s no clear standard for that today.
So I built one.
What we’re building
EbbScore — a Retirement Score (300–850)
Think of it as a simple way to measure how someone is doing financially for retirement, in one number.
Users:
- Get a free score
- See hidden fees in dollar terms
- Understand what’s helping or hurting them
- Get a prioritized action plan
We don’t manage money and don’t sell financial products.
We’re positioned as an independent layer focused on transparency.
Traction (early but real)
- Live product with full scoring + report flow
- Users can upload statements or connect accounts
- Early traction driven by content + Reddit (multi-million view reach across posts)
- Strong engagement around fee transparency and “am I on track”
Why this could work
The score itself isn’t the main value. The data layer is.
We’re building a dataset of:
- Retirement plans
- Fund fees
- Allocations
- User-level outcomes
Each new user improves the model and expands the dataset.
If this works, it becomes a standardized way to measure retirement readiness across individuals and plans.
Business model
B2C:
- Free score
- $7.99/month monitoring
- Paid reports
B2B (early):
- Employer plan scoring
- Fiduciary risk / compliance positioning
- This also leads to mass adoption on B2C side
Future:
- Benchmarking and data insights layer
- Platform opportunities
Raise
Raising a $250k–$500k equity round via SAFE to:
- Improve scoring accuracy and dataset depth
- Expand distribution (content + partnerships)
- Build monitoring and engagement features
- Develop employer side product
Focused on bringing on partners who understand long-term data-driven platforms.
Why me
- 10+ years in financial services
- Managed ~$100M in client assets
- Built from firsthand experience with how opaque retirement systems are
What I’m trying to validate
- Does this become habit forming or a one time check?
- Is the data moat real and defensible?
- Can this become a standard layer in retirement decision-making?
Ask
Looking for:
- Feedback (especially on moat and distribution)
- Investors interested in early-stage fintech/data platforms
- Critical perspectives — where does this break?
I’ll respond to everything publicly.