For people signing offers, not raising rounds

An option grant is a headline with the math removed.

“22,000 options” fits in a sentence. The cap table, the strike price, the $75M of investor money paid out before you, and the tax bill don’t. Grantwise puts them back, runs four exits, and hands you the questions to ask before you sign.

Free. No account needed. Not tax, legal, or investment advice.

Worked example

Series B senior engineer

22,000 options at $1.60 · ~0.153% after dilution

Net after tax

Acqui-hire

Common downside

$0

Acquisition

Solid exit

$51.9K

Modest IPO

Good public outcome

$775K

Great IPO

Best case

$3M

Edit these assumptions

Offer moments

Built around the moments when equity gets confusing.

The same calculator should not treat a seed engineer, a Series B hire, and a late-stage manager like they are asking the same question. Grantwise changes the emphasis as the offer changes.

01

Big upside, missing denominator

Seed grant

A large option count can be real leverage or just a large-looking number. Ownership and future dilution decide which.

Assumptions surfaced

ownership %future roundsAMT
Model a seed offer
02

The strike starts to matter

Series A/B offer

Later grants often look cleaner, but preferences and exercise costs can quietly remove a lot of practical value.

Assumptions surfaced

strike costpreferencedilution
Open worked example
03

Safer story, tighter upside

Late-stage package

At high valuations, the brand name is less important than how much growth is still left after tax and exercise.

Assumptions surfaced

taxexit rangeexercise
Stress-test late stage
04

Positive headline, weak payout

Acqui-hire case

A company can sell, the press can sound fine, and common shareholders can still receive very little.

Assumptions surfaced

common poolpreferencenet outcome
See the downside

What the model actually does

Built on the parts offer letters leave vague.

Grantwise is deliberately narrow: stock option grants, real assumptions, and no fake precision. The point is not to predict an exit. The point is to reveal what has to be true for the grant to matter.

Read the methodology

Scenario engine

Acqui-hire, acquisition, modest IPO, and great IPO are shown side by side instead of collapsed into one optimistic number.

Dilution model

Use a simple dilution assumption or add future financing rounds when the offer needs a more realistic path.

Tax context

Country presets, custom effective rates, and US ISO AMT sensitivity sit next to the outcome they change.

Preference waterfall

Lower exits pay preferred shareholders first, making the common pool visible before your grant participates.

Shareable brief

Export or copy a concise summary you can use when asking recruiters for the missing denominator.

Equity waterfall

How exit value becomes employee net value

Acquisition

How exit value turns into estimated employee net value after preference, exercise cost, and taxes.

Exit value$150M
Less preferred liquidation preference-$75M
Common value pool$75M
Employee gross option value$115.1K
Less exercise cost-$35.2K
Less estimated tax and AMT-$44.3K
Employee net outcome$51.9K

From offer letter to negotiation brief

Three steps that make the number usable.

The interface is designed to move from raw grant details to a summary you can actually discuss, without accounts or saved personal data.

Option count
Strike price
Ownership

Create the grant

Paste the option count, strike price, valuation, and either ownership or fully diluted shares.

28%

Set the conditions

Tune dilution, preference, tax country, exercise window, and scenario mode until the assumptions match reality.

Use the result

Leave with four outcomes, a confidence level, negotiation questions, and an exportable brief.

Scenario proof, not testimonials

Realistic stories from offers people actually receive.

The examples are fictional, but the mechanics are not. Each card shows the kind of surprise Grantwise is built to surface before the offer becomes your problem.

$5K

AMT sensitivity

$92.7K

Late-stage modest IPO net

12

Questions generated

0

Backend data stored