Nettaker is an iOS app for digital nomads, freelancers and anyone weighing a move. People compare gross salaries when they should compare what they actually keep. Nettaker takes a salary and a country and returns real net pay, a clear tax breakdown, the local cost of living, and a ranked comparison of destinations — with verified, bracket-level data where it is sourced and a clearly-labelled estimate everywhere else, across 133 countries.
The challenge
The real problem was never the calculation — it was decision uncertainty wrapped in financial intimidation. People need to trust a number enough to make a life decision on it, but most tools either oversimplify (and feel untrustworthy) or drown the user in tax jargon (and feel unusable).
- People compare gross salary instead of real take-home
- Tax and social-contribution systems differ wildly by country
- Existing tools are either too basic to trust or too technical to use
- Relocation decisions are high-stakes and emotionally loaded
- Users need confidence — which means seeing enough of the breakdown to believe it
- Cost of living changes the answer as much as tax does
Discovery & research
Before a single screen, the real risk was trust: people don't act on a take-home number they can't believe. Discovery focused on why existing tools fail, what data could be sourced credibly across countries, and how much detail a person actually needs to make a relocation decision with confidence.
Existing tools fail at opposite extremes
Mainstream salary calculators lead with gross or a single headline rate; official tax portals are accurate but bury the answer under jargon and forms. One side is believable-but-useless, the other usable-but-opaque — and neither helps someone decide. That gap defined the brief: a fast, believable answer first, with the evidence available on demand.
EvidenceTeardown of gross-salary calculators, national tax portals, and relocation cost indices (Numbeo-style).
People compare the wrong number
The decision underneath the search — 'would my life actually be better there?' — turns on net pay after tax and social contributions, then on local cost of living. Gross salary, the figure everyone quotes, is the least decision-relevant of the three. The product had to walk the user from gross, to net, to net-after-living-costs.
EvidenceJobs-to-be-done framing of the relocation / remote-work decision.
Trust is the conversion mechanic — and it comes from honesty about the data
A financial answer a user cannot interrogate is one they will not act on. The conclusion was to make data provenance visible rather than hide it: show verified, bracket-level models where they could be sourced, and clearly label estimates everywhere else. Never a confident-looking number with nothing behind it.
EvidenceShipped as a 133-country registry with verified bracket models plus explicitly labelled estimates, surfaced by a verified-data badge in the UI.
Tax systems do not generalise — each had to be modelled
Brackets, allowances, and social-contribution rules differ enough that one global formula would be wrong almost everywhere. Discovery built a per-country data model — bracket-level where verifiable — instead of an approximation, which set the architecture for the entire calculation engine.
EvidencePer-country tax JSON bundled in the app; 169 automated tests over the calculation layer.
Cost of living moves the answer as much as tax does
A higher net salary can still be a worse life if rent and essentials cost more, so 'money kept' had to mean kept after living costs. Cost-of-living context was scoped in from the start as a first-class part of the comparison, not a later add-on.
EvidenceCost-of-living model in the product, with optional live-index integration.
How I approached it
I reframed the calculator around the real job-to-be-done — making a life decision with confidence — and made the net figure the hero. Then I designed the trust ladder: a fast answer first, an honest breakdown on demand, cost-of-living context, and a side-by-side comparison that ranks countries by what you actually keep. The whole thing sits in a calm lavender Liquid-Glass language with monospaced numerals so figures stay legible and stable.
Defined the real decision behind the calculator, not just the formula
Made net take-home the headline, with effective rate and monthly figure beside it
Designed a tax breakdown (donut + rows) that explains without overwhelming
Made country comparison a first-class action, ranked by money kept
Added a cost-of-living model so "kept" means kept after living costs
Used monospaced numerals and a verified-data badge to build trust
Trade-offs
Trust was the whole game. A financial answer the user does not understand is one they will not act on — but a financial app that explains everything at once is one they will not use. The design had to sequence complexity and signal accuracy without ever feeling like formal tax advice.
- Financial information feels intimidating by default
- Each country has different deduction and contribution logic
- Comparisons must feel simple but credible
- Verified vs estimated data has to be unmistakable
- The product must help decide without posing as tax advice
Final direction
The final model is Capture → Explain → Compare → Decide. A focused input returns a fast net figure; the breakdown, cost-of-living and comparison unfold on demand. Compare ranks destinations by take-home (or money left after living costs) with a clear winner — and the whole app, lavender Liquid Glass and all, ships with a fully designed dark mode.
Outcomes
Nettaker is a built, App-Store-ready iOS app — 133 countries with verified bracket-level tax models and honestly-labelled estimates, a cost-of-living model, 169 tests, and App Store submission metadata (fastlane + StoreKit) in the repo — not a prototype. The design contribution is a trust model for financial decisions: net-first, honestly sequenced, with verified-vs-estimated made visible. [Add a live metric once shipped: activation / comparison-completion rate.]
One system, every screen
People don’t need a smaller number. They need a number they can believe.
Clarity is not the absence of complexity. It is the careful sequencing of complexity — answer first, evidence on demand, trust throughout.