Pre-Revenue Valuation in Deep Tech: How to Price What Doesn’t Exist Yet — Part 1
How do you build a pre-revenue valuation in deep tech, where ARR doesn’t exist and the “market” still needs to be educated?
A fusion reactor, a drill that tunnels 20 kilometers into the Earth’s crust, a pharmaceutical capsule that manufactures in orbit—the technology exists, the patents are in place, but the sales? Still none.
So what then?
How do you value a Deep Tech Startup before revenue?
Valuing a company before revenue is one of the most intricate challenges in venture finance. Without sales or EBITDA, investors must base their decisions on potential, not performance. As Investopedia notes, “for startups with little or no revenue… assigning a valuation is particularly tricky.”
Deep tech startups push this challenge even further. They are often built on radical technologies or entirely novel processes, making reliable comparables virtually impossible to find. A company working on geothermal infrastructure or space-based manufacturing may have no direct competitors, no sales, and no meaningful benchmarks—rendering traditional valuation tools largely ineffective.
Industrial deep tech ventures demand vast capital and lengthy development. They often spend years on R&D and prototype testing before any sales, and may require governmental or strategic partners. This contrasts with a software startup that can quickly iterate and attract early users. As a result, deep tech valuations must account for high technology risk, long timelines, and capital intensity.
For example, a fusion startup like Commonwealth Fusion Systems has raised $1.8 billion without selling any electricity, based on its expected breakthrough impact. Similarly, Varda Space—which aims to manufacture drugs in orbit—raised $145 million by 2024 after only one demonstration mission. These sky-high financings underscore that investors are betting on potential scalability rather than current profits.
Because revenue projections are so speculative, many traditional methods—such as discounted cash flow (DCF) or market multiples—are unreliable. Instead, investors often use qualitative frameworks that reward strong teams, technology milestones, and large market potential. They may also adjust valuations with risk scores or scenario analysis. All of these approaches are subjective to some degree, but they provide a structured way to anchor a valuation even when hard numbers don’t exist.
The key is to be systematic and transparent about assumptions—an absolute necessity when you’re selling something that, technically, doesn’t exist yet.
If you're preparing to raise Seed or Series A capital—or seeking corporate venture funds, strategic partnerships, or government financing—your valuation framework must reflect the true nature of your industry. It should quantify your current level of technical maturity, define the milestones ahead, and explain how each of those milestones maps to clear value inflection points.
This guide unfolds in two parts:
Part One—what you’re reading now—breaks down the core valuation methods for pre-revenue startups and shows how to apply them in deep tech: high capex, long cycles, no revenue. You’ll learn how to choose the right method for your stage and investor using a situational matrix, and how to triangulate multiple approaches into a credible valuation range.
Part Two moves from theory to execution. We’ll apply these methods to realistic deep tech startup scenarios — from grid storage to orbital manufacturing — and walk through how founders can craft a valuation story that holds up in the room. You’ll see how different frameworks play out in actual investor conversations, how to tailor them by risk profile and stage, and how to anchor value to milestones, comparables, and long-term potential.
Let’s get into it.
Common Pre-Revenue Valuation Methods
Valuing a company before revenue is, by nature, an imprecise exercise—especially in deep tech, where long timelines, high uncertainty, and minimal market data are the norm.
Over time, a handful of frameworks have emerged to make this process more structured. None are definitive. All are subjective to some degree.
But they offer a way to anchor conversations in logic rather than instinct.
These models blend qualitative assessment—of the team, the technology, the progress made—with lightweight financial structure. Each serves a different use case, depending on the maturity of the startup, the availability of comparables, and the risk appetite of the investor.
Below is a summary of the 5 most commonly used pre-revenue valuation methods in Deep Tech: