Wizards & Decision Trees

Picking the right Wealth Data Set out of the full compliance, tax, retirement, and estate catalog usually takes a long conversation. Answer a few questions about your feature and get a named bundle recommendation.

'How realistic does the data need to be' is the wrong question — both can be made realistic. What matters is legal-risk tolerance, edge-case coverage, and how closely the environment gets examined.

Build-vs-buy for synthetic wealth data isn't a cost question, it's depth and cadence — nail the demographics but skip regulatory tracking and the corpus ages out fast. Answer a few quick questions to find out which fits.

Production bugs in wealth-tech usually trace to a synthetic case missing from the test corpus. Pick the symptom — wash-sale miss, RMD miscalc, IRMAA misclassification — and get the archetype that reproduces it.

Reg BI exam outcomes are predictable from your test corpus — an examiner checks whether recommendation rationale and senior-client review are structurally captured. Find the gap before the examiner does.

PMs and engineers usually sense a feature has a compliance angle before they know which workstream applies. Answer what the feature touches and get the workstream that belongs on the spec from day one.

Edge-case coverage isn't one decision, it's one per code path — TLH lot-selection, RMD aggregation, the IRMAA cliff, Roth conversion, NUA, QSBS. Pick the path and get the archetype slice that exercises it.

A synthetic corpus that doesn't refresh becomes a liability — tax brackets move every year, Reg BI sweep findings shift the bar, new features open new edge cases. Match regulator exposure to a schedule that keeps pace.

Run the free open benchmark, build your own harness, buy a ready-made decision corpus, or request a custom spec — four honest paths with different tradeoffs. Answer a few questions and get routed to the one that actually fits.