The right question isn’t whether your number is correct. It’s whether it holds up in the room when you raise.
A founder asked us a question recently that gets at something real. He had run his valuation, gotten a number he liked, and then paused before relying on it. “How reliable is this? If I go and raise, will it back up that my valuation is correct?”
It’s the honest version of the question every founder has about a startup valuation. And the honest answer is one most valuation tools won’t give you: there is no “correct” to back up.
That sounds like a dodge. It isn’t. It’s the most useful thing we can tell you, because once you understand why no valuation can promise correctness, you stop asking the wrong question and start asking the one that actually decides your round.
There is no single true number, even for the companies you’d think have one
Start with the companies that supposedly have the cleanest valuations: the big, late-stage, heavily-scrutinized ones. If accuracy existed anywhere, it would exist there.
It doesn’t. When two Stanford researchers rebuilt unicorn valuations using a proper financial model, they found that reported valuations sat about 51% above their fair value on average, and 53 of the 116 unicorns they studied lost their unicorn status entirely once the math accounted for the special rights late investors get. The headline number gets calculated by taking the price of the most recent, most protected share class and applying it to every earlier share as if they were the same thing. They aren’t.
This isn’t a fringe finding. Stanford’s own teaching note on venture financing states plainly that the post-money valuation is typically higher than the fair value, with far-reaching practical implications. The most quoted, most “objective” numbers in the entire market are negotiated representations, not measurements.
So when a tool implies it can hand you an accurate valuation for a company with no revenue, three years of history, and a spreadsheet of projections, your skepticism is correct. We share it. We built a platform that produces valuations for a living, and we will never tell you the number is accurate. We can’t, and neither can anyone else being honest with you.
A valuation is a story, and the question is who believes it
Here’s how we think about what the number actually is.
A valuation is the representation of a story about what your company is going to do. You tell that story to the platform through your inputs: your stage, your traction, the risks you’ve cleared, where you think the financials go. The model turns that story into a number using a defensible, public method. But the number is downstream of the story. Change the story honestly and the number changes honestly with it.
This is also why a discounted cash flow model on an early-stage company can spit out a present value that’s very low, sometimes even negative. That isn’t a glitch. It’s the math faithfully representing a story where most of the value sits years out and the near-term risk is high. The assumptions drive everything, which is exactly the point.
The person who has said this best isn’t us. The valuation scholar Aswath Damodaran describes a good valuation as a marriage between stories and numbers, and he’s blunt about young companies: “You’re definitely wrong. You don’t have to be right to make money. You just have to be less wrong than everybody else.” That’s the whole game. Not right. Less wrong, and defensible about why.
So the question “is my valuation correct?” quietly becomes a better question: does the story behind my number hold up when I tell it to an investor? If the story you told the platform is also believed by the investor across the table, they’ll believe the valuation. If it isn’t, no amount of decimal places will save it. A valuation is a shared language for a negotiation, and a negotiation needs two people to agree on the same story.
The one outcome that actually matters
We’re suspicious of accuracy claims, so we don’t measure accuracy. There’s nothing honest to measure it against. What we can measure is whether the report did its job in the moment that counts.
After founders raise, we ask them a simple question: was the valuation report useful in the negotiation? In our surveys, 94% said yes (Equidam internal data).
We want to be clear about what that number is and isn’t. It is not a claim that the valuations were “right.” It’s a measure of usefulness in the room. Given how hard those conversations are, and how often founders walk in without a defensible basis for their ask, we think it’s a strong result. It’s the metric that maps to the real question. Not “was the number correct” but “did it help you hold a position with someone whose job is to push back on it.”
That distinction is the entire reframe. Trust in a valuation shouldn’t come from believing it’s accurate. It should come from knowing it’s defensible: that every input traces to a method you can explain, that the comparables are real, that an investor poking at it finds reasoning rather than a black box.
That’s what we build for. The valuation runs through five methods, two that price risk and three that price return, weighted by your stage, against a base of 160,000+ valued companies and 30,000+ public-market comparables, across 90+ countries and 600+ industries. The methodology is public and aligned with international valuation guidelines. Not so we can claim the output is correct, but so that when an investor asks “where does this come from?”, you have a real answer instead of a shrug.
On bias, and why we won’t claim to have solved it
There’s a harder part of the trust question, and the tools that promise to “remove human bias” usually skip it.
Bias in startup financing is real, and it’s measurable. A 2025 study from the National Bureau of Economic Research found that after a prior startup failure, women founders went on to raise 53.3% less capital than their male cofounders, and even after a prior success they still raised 24.6% less, with the gaps traced to how investors treated them rather than to the quality of the companies. At the market level, female-founded and cofounded companies took 19.9% of US venture deal value in 2024, while all-female-founded fintechs drew roughly 1% of fintech deal value. This is the water the whole industry swims in.
So here is what we do, and what we don’t.
We deliberately build the model only from inputs grounded in valuation research and literature. And we deliberately leave out inputs that would import bias for no analytical reason. We don’t ask a founder’s gender. We don’t ask things that correlate with who you are rather than what you’re building. The questions are about the company and its risk, because that’s what valuation literature supports.
But can we promise the resulting valuation is bias-free? No. We won’t pretend otherwise. A valuation leans on market comparables, and if the market that generated those comparables is biased, some of that bias travels into the data. Investors carry their own biases into the negotiation, and the valuation has to survive contact with them. We push hard against bias at every point we control. We don’t get to claim we’ve eliminated it, because the inputs we don’t control still carry it.
We’d rather tell you that than sell you a clean story we can’t back up. A tool that claims to have removed bias entirely is making the same kind of over-promise as one that claims to be accurate. Both are betting you won’t look closely.
What this means for your raise
Back to the founder’s question. If you raise, will the valuation back you up?
It will back you up to exactly the degree that the story behind it is credible and defensible. The number isn’t a fact you’re presenting; it’s a position you’re taking, supported by a method you can walk through. The valuation gives you the structure and the shared language to have that conversation. Whether the position holds depends on the story being honest and the investor finding it believable. Those are things you can actually work on. “Accuracy” was never one of them.
A few honest caveats, because this whole piece is about honesty. We don’t have perfect insight into why any single round closed at the price it did; negotiations are private and messy. The 94% figure is our own survey data, measuring usefulness rather than correctness, and we’ve kept that distinction explicit on purpose. And no method, ours included, escapes the biases baked into the market it draws from.
What we can offer is a number you can stand behind: built from a public methodology, grounded in a large base of real comparables, and structured so an investor can interrogate it and find reasoning at the bottom. That’s not accuracy. It’s defensibility, and in a negotiation, defensibility is the thing that actually holds.
If you want to see what a defensible number looks like for your own company, you can run a valuation on Equidam and tell your story for yourself.