ATHARVA BARAD
Projects / Product economics
Early product / finance project · Four-person team

Visionaire: Turning a Product Idea into Unit Economics.

An early smart-glasses business-plan project where my work focused on translating an ambitious product concept into price, startup cost, revenue, financing, and break-even assumptions.

Source note: the original business-plan and pitch files have not been recovered. The figures below come from preserved project evidence in my prior resume archive, so I treat them as historical planning assumptions rather than recreating missing materials.

What I owned

Within the four-person team, my documented ownership centered on pricing, financing, startup-cost assumptions, revenue and expense projections, and break-even analysis.

The useful question was economic, not futuristic

The useful part of the project was not the headset concept by itself. It was translating a speculative product into a set of economic questions: what price could support the cost structure, how many units would be required to recover fixed investment, and how sensitive the business became to adoption.

What the model said would have to be true

25,000–45,000 units over three years · $10.75M–$19.35M projected revenue · $7.525M–$13.545M projected gross profit.

The preserved project evidence also records a 34,150-unit break-even point and a modeled 60–80% ROI range. These were project assumptions/projections, not realized commercial outcomes.

What I can defend from the surviving evidence

The preserved numbers are enough to show the structure of the business question: a 25,000–45,000 three-year unit range, $10.75M–$19.35M projected revenue, $7.525M–$13.545M projected gross profit, and a modeled 34,150-unit break-even threshold. What I cannot defend without the original workbook is the detailed lineage behind every assumption, so I do not present those figures with more precision than the source archive supports.

Why I still include this earlier work

The project shows an early version of a pattern that became much stronger in later work: turn a broad idea into explicit variables, thresholds, and decision constraints so the discussion can move from “is this exciting?” to “what would have to be true for this to work?” The later Costco, Apple, UniPath, and Monte Carlo projects are more rigorous—but the instinct is already visible here.