From first number to signed term sheet - how founders use Evaldam

Revenue is growing but the proposed pre-money valuation feels like a guess. The founder needs a defensible number before VC calls.

Runs a six-method valuation, models dilution at multiple pre-money scenarios, and exports a report showing low/base/high with documented assumptions.

Walks into investor conversations with a range they can defend - and a shareable report that does the explaining.

The team has a prototype and regulatory plan but no ARR. Traditional revenue multiples aren't applicable yet.

Uses Scorecard and Berkus-style logic to quantify idea clarity, prototype quality, market size, and execution risk - then asks the AI assistant to explain each factor to co-founders.

Creates a structured starting point for angel conversations instead of a number pulled from thin air.

Strong GitHub traction but no paying customers yet. The founder needs to translate repo signals into a valuation that angels will take seriously.

Runs the GitHub valuation preview first, then adds early user interviews and market data in the full workspace to produce a blended six-method range with clear assumptions.

Goes into seed discussions with a defendable number and a report that shows exactly how the repo evidence was turned into a range.

A public OSS repo has product potential but adoption and monetization are early. Investors ask for a valuation and the founder doesn't know where to start.

Separates repo traction from company value, runs a full startup report with customer, market, and monetization assumptions, and tracks the valuation as paid seats grow.

Identifies the milestones that move the needle on fundability - and has a report ready for each update.

An investor has sent a term sheet. The founder needs to understand the real dilution impact before signing - and wants data to negotiate valuation.

Models post-money ownership across multiple pre-money scenarios, runs sensitivity on ESOP pool sizing, and uses the AI assistant to work through liquidation preference implications.

Goes into negotiation with numbers, not instinct - and closes at a valuation they actually understood.