The Comparable-Round Set: Sanity-Checking a Valuation Ask

You have a valuation ask on the table and need to decide whether the number is fair before you take a view to your investment committee. This guide is for early-stage investors, platform and talent partners at funds, and angels who want to build a set of genuinely comparable recent rounds from public signals, adjust for how the target differs, and turn that set into a defensible valuation range and a negotiation stance. The founder-facing best answers explain valuation methods to people setting their own ask. This flips it to your side and treats comp-building as what it actually is: a search task, then a filtering task, then a small piece of arithmetic you can defend line by line.

What makes a round set defensible rather than decorative

A defensible comp set is 5 to 8 recent rounds, sourced from filings rather than press, converted to one valuation basis, and traceable deal by deal so it survives someone else's counter-comps. The test is not whether your number is right in some absolute sense. It is whether the set holds up when a skeptical partner tries to break it.

Comparable company analysis for a startup valuation borrows its discipline from three adjacent worlds that have argued these questions in front of judges and regulators. Real-estate appraisal under Fannie Mae requires a minimum of three closed comparable sales within the last 12 months. M&A practice caps precedent comps at 24 months for serious work and insists the trend be analysed, not just the level. Delaware appraisal litigation, in cases like DFC Global Corp. v. Muirfield Value Partners, rejects comp sets with fewer than five deals and sets where the largest deal runs more than 5x the median. None of these are venture rules. There is no securities regulator or venture body that sets a binding recency window or minimum set size for private funding-round comps. Treat that borrowed standard as load-bearing, and flag it as borrowed when you present it.

The comps do not have to be perfect. They have to be defensible against the assessor's counter-comps.

The reason this matters more in venture than in real estate is that private markets re-rate fast and hard. A comp that felt current a year ago can be stale by a full turn today, and staleness cuts both ways depending on the cycle. Get the set right and the arithmetic that follows is almost mechanical. Get the set wrong and no adjustment method will save the number.

The public signals that pin stage, sector, geography and traction

The primary legal signal is SEC Form D. When a company raises a US venture or private-equity round, it must file a Form D with the SEC within 15 days of the first sale of securities, and each filing carries the offering amount, equity type, number of investors, date of first sale, and total sold to date. First-time filer status is often a clean proxy for a first institutional round.

Form D matters because press does not cover the market you are pricing against. Roughly 90% of seed rounds never receive top-tier press coverage. If you build your comp set from TechCrunch or LinkedIn announcements, you are systematically blind to the majority of the market, and the fraction you can see is self-selecting toward the larger, hotter, announced rounds. That skews your baseline up before you have adjusted anything. Filings appear on average 7 to 14 days before any press, so the filing is both more complete and earlier.

Commercial round databases sit on top of the filing base and add the filters you need to build a candidate list: industry, location, stage, revenue, valuation and investors. The most common are the general-purpose round databases; use them to enrich and cross-check, not as your only source. The discipline is to anchor on the filing, then let the database tell you sector and traction. Where you are trying to find the founders behind a filtered slice of the market fast, plain-language search over an index of professional profiles collapses hours of database filtering into one query. Refolk is built for exactly that kind of ask.

The current baselines you are pricing against

Anchor to the Carta stage and sector median, and carry the exact figure with its period and basis so nobody in the room can dispute what you started from. The published venture benchmarks below are the market baseline before any adjustment for how your target differs.

Table A. Carta median valuations by stage, 2025.

The direction of travel is up. Median seed pre-money on Carta reached a new high of $16M in Q3 2025, up 14% year over year, and median Series A pre-money hit an all-time high of $49.3M in the same quarter, having already reached $48M in Q1. On a post-money basis the median seed valuation is $20M, and the 95th percentile hit $80.5M in 2025, nearly 3x the $28.5M 95th percentile in 2019. Startups on Carta raised $27.3B in Q3 2025, the highest quarterly sum in three years.

Two structural facts change how you read a raise amount. Median Series A dilution fell from 20.9% to 17.9% in a year, so the same check now buys less equity at a higher valuation, which means comparing raise amounts without valuations misreads the stage. And the down-round rate fell from roughly 19% in Q1 2025 to under 12% by early 2026, which tells you the cycle is rising, not falling, when you decide which direction to time-adjust an old comp.

Segment AI from non-AI before you touch the median

The single biggest distortion in a current comp set is the AI tag, not fundamentals. A comp misclassified as AI moves your Series A baseline by roughly 38% and your late-stage baseline by up to 193%, so segment AI and non-AI comps into separate columns before you compute anything.

Table B. AI vs non-AI valuation premium, 2025.

The mechanism is capital concentration. At every stage from Series A onward, AI startups raised larger rounds at higher valuations than non-AI peers in 2025. At Series D, 58% of all cash raised went to AI startups, and more than 60% of all venture on Carta in Q1 2026 went to AI companies. The blended market median is being pulled by a distinct sub-market. If your target is not an AI company, an AI-heavy comp set will hand you a baseline a full stage-premium too high, and you will walk into your IC defending a number the market never priced for a company like this one.

Whether a defensible set is even reachable in your sector

Before you spend two hours pulling candidates, check whether the pool is deep enough to reach five survivors, because comp pool depth varies by roughly 3.5x across sectors in the same country. A thin sector forces you toward the five-deal floor where Delaware-style rejection risk is highest.

Table C. Founder supply by market and sector, from Refolk's index.

In Refolk's index of professional profiles, a US fintech seed has 1,344 candidate founders behind it versus 389 for a US AI company, so your fintech comp pool is roughly 3.5x deeper. That difference decides your risk posture. A deep pool lets you build 6 to 8 tight comps and discard the borderline ones. A thin pool leaves you at the floor, where every included and excluded deal has to be footnoted.

Cross-border sets are structurally thinner still. The UK AI founder pool is 130 in the index, roughly one-third the US depth, which means UK comp sets breach the five-deal threshold more often and frequently need US comps time-adjusted and geography-adjusted in. Know this before you promise anyone a clean domestic set.

The procedure, start to finish

Run these seven steps in order. Analyst time is noted at each stage; the whole pass is a focused half-day for one person, plus an hour of partner time on the adjustment.

One note on ordering. Some founder guides run the Scorecard adjustment before fixing a set, starting from a regional average, because the founder is pricing their own company. Investor practice inverts this: fix the comp set first, establish the baseline from it, then adjust. That keeps your number traceable to specific deals rather than an abstract regional figure, and it is what survives an IC challenge.

The Scorecard adjustment, in numbers

The documented adjustment route is the Bill Payne Scorecard method. It starts from the average pre-money valuation of comparable companies in the region, sector and stage, then adjusts for how the target compares on weighted factors.

Mechanically, you score the target on each factor and multiply the weighted total by the peer average. If the average pre-money is $10M and the target scores 80% on team, 70% on market, 90% on product, 60% on traction and 50% on risk, the weighted result lands at roughly $7.15M pre-money. The output is a point estimate; the range comes from the stress-test in the final step.

Company | Stage | Sector | AI? (Y/N) | Geography | Round date | Amount raised | Pre-money | Post-money | Source (Form D / DB) | Include? | Reason if excluded

How this goes wrong

Most bad comp sets fail in one of seven predictable ways. Each has a false positive it produces and a specific check that catches it. This is the part of the work worth slowing down for, because a clean-looking number resting on a broken set is worse than no number at all.

A few of these deserve extra weight. Stale comps are the most common single error and the most dangerous right now because the market is rising. With seed medians up 14% year over year, an old comp understates as often as it overstates, which is the opposite of the 2022-to-2023 down cycle. That makes upward time-adjustment defensible today rather than a thumb on the scale. A 2021 comp can sit two to three turns above today's reality if you leave it unadjusted, so re-rate it explicitly and show your work.

The thin-set failure hides behind confident presentation. A number computed from three rounds looks identical on a slide to one computed from eight. The remove-one sensitivity test is what exposes it: pull each comp out in turn and recompute. If the indicated value changes materially when any single deal leaves, disclose that sensitivity, because your conclusion depends on one uncertain sale. Delaware rejects sets under five deals for exactly this reason.

Basis mismatch is the quiet one. Mixing pre-money and post-money figures manufactures an apparent 20 to 25% valuation gap that is nothing more than the round size. Post-money equals pre-money plus the amount raised, so convert everything to one column before you compare a single pair.

What good looks like before you take it to IC

Run this checklist before you call the set finished. Each item is a thing to verify, not a topic to think about. If any fails, you have more work before the number is defensible.

The output you carry into the room is three things: a range, not a point; a stance versus the ask stated plainly, such as "the ask sits above our set's 75th percentile and we would push toward the median"; and the record table itself, so anyone can trace the range back to named deals. A range without the underlying rows is an opinion. A range with the rows is an argument.

Keeping the set current

A comp set has a short shelf life because the medians it rests on move every quarter. Re-anchor the baseline against the latest Carta State of Private Markets release before you reuse an old set, and re-check the down-round rate and dilution figures, since both shift the direction and size of your time-adjustment. A set built when the down-round rate was 19% carries different assumptions than one built when it is under 12%.

The mechanism to watch, rather than any single number, is capital concentration in AI. As long as more than half of venture dollars flow to AI companies, the blended market median will keep drifting away from the median a non-AI company actually faces. Re-check the AI share each quarter and keep your segmentation honest. When you need to rebuild the candidate pool quickly for a fresh set, searching an index of professional profiles in plain English gets you from a subject profile to named recent-raise founders faster than rebuilding database filters from scratch, which is where most of the half-day actually goes.