Where it started
This began as a personal deep-dive into one real, high-stakes retirement scenario — the kind where the interactions between required minimum distributions, Roth conversions, Social Security taxation, and Medicare’s IRMAA surcharges create six-figure swings, and where simpler planning tools quietly fall apart.
The brief was blunt: not “you’ll be fine,” but a defensible, year-by-year roadmap someone could actually act on.
That’s the real subject of this project. The orchestration came later — it exists to serve this, not the other way around.
Trust the numbers before you write a word
Before any planning, I treated the underlying financial model as untrusted input and stress-tested it. That verification pass surfaced three material modeling defects in the planning workbook as configured for this scenario — issues that would have overstated or understated taxes by six figures across the planning horizon if left unchecked.
The lesson stuck and shaped everything after it: the highest-value use of AI here isn’t the headline writing — it’s the unglamorous verification work. A plan is only as good as the numbers under it, so the numbers get checked first.
From one plan to a repeatable system
A polished retirement plan is valuable once. A system that produces defensible, consistent plans — anchored to the numbers every time — is valuable on every scenario after it. So the real project became reverse-engineering that one deep-dive into a repeatable pipeline.
From a finished financial model, it writes a complete, nine-part analysis — Roth conversion strategy, year-by-year tax planning, income sequencing, annuity review, legacy-stock cost basis, Medicare and IRMAA, liquidity, longevity and estate, and a delivery roadmap — and assembles it into one polished, cross-referenced document. The point was never “have AI write a retirement plan.” It was to build the discipline around the AI so the output is reliable enough to hand to a person and stand behind.