The Public-Signal Momentum Score for Early-Stage Triage

You spotted a name. A scout mentioned it, a product launched, or a search surfaced it, and now you have to decide whether it is worth a warm intro before you know anything private. This guide gives early-stage investors, fund platform and talent partners, and angels a fixed set of public momentum dimensions, a threshold for each, and a way to combine them into a defensible pursue-now, watch, or pass call with a written rationale. It is the triage judgement you make dozens of times a week, reduced to a score instead of a gut feel.

This is not the sourcing process that finds the name, and it is not the founder grade. It is the company-level call you make in the gap between spotting a startup and spending scarce social capital on it, using only what is public.

Why a fixed score beats a gut call at triage

A fixed score wins because it makes dozens of weekly triage calls comparable, and comparability is the whole job at this stage. The documented practice is a crude weighted scorecard: rate founder, market, traction, economics, and exit on a 1 to 5 scale. As one angel training source puts it, a scorecard minimizes emotional bias and promotes deal homogenization. The value is not that the scorecard is smarter than you. It is that it forces every company through the same filter, so a strong signal in one deal is measured against the same yardstick as the next.

The economics of triage make this urgent. A documented seed funnel screens 2,000 companies to reach 500 partner meetings and 12 investments. You cannot spend real time on 2,000 names. Research puts partner screening research at zero to fifteen minutes per company, a deck scan at two to five minutes, and the raw meet or no-meet reflex at under two minutes. Whatever you use to decide has to run inside that window.

Here is the edge. The triage window is short, but the best public signal has a long lead time. GitHub engineering acceleration precedes a fundraise announcement by three to six weeks. So a pre-built score lets you act inside a window the crowd has not priced yet, on evidence that is public but not obvious. That is the case for building the score once and running it fast, rather than re-deriving your opinion from scratch each time.

The five momentum dimensions and what each proves

Score five public dimensions on a 1 to 5 scale: founder, market, traction, hiring velocity, and engineering velocity. Each proves something specific, and each has a way it lies. Naming the lie up front is what keeps the score honest.

Founder. What it proves: pattern-matched execution odds. Serial entrepreneurs show roughly 30 percent success versus 18 percent for first-timers per Gompers and colleagues, so prior founding is a real, if blunt, signal. How it lies: a strong resume with no evidence of shipping. Look for public work, not just titles.

Market. What it proves: whether the demand is real and large. How it lies: a founder-supplied total addressable market number in a deck. At triage you have no deck, so infer the market from third-party evidence, not projections.

Traction. What it proves: demand that persists. How it lies: a launch-day spike. The clean test is whether the number grows when the company stops pushing. Retention is the durable form: median net revenue retention for venture-backed B2B SaaS is 106 percent, and under 100 percent stalls most Series A talks.

Hiring velocity. What it proves: intent and capacity to scale, but only weakly on its own. How it lies: a live hiring page on a stalled company. A 2023 analysis found no statistically significant headcount-growth difference between top-quartile and bottom-quartile fundraisers, so weight this below velocity and retention.

Engineering velocity. What it proves: real building. How it lies: commit noise in AI-only repos and star counts that create outdated zombie leads. Read contributor growth and deploy cadence, not stars. A contributor count jump of 50 percent or more usually means a round just closed.

Growth thresholds by stage, and where they break

Score traction against the stage-appropriate month-over-month bar, but treat any rate off a tiny base as directional only. The strong bar at seed is 15 to 20 percent or higher month-over-month in the key metric. Below a real base, a single month tells you almost nothing.

The pre-seed row carries a warning. As one seed fund puts it, your month-over-month growth can look erratic: you might jump 300 percent after landing one customer, and then flatline the next month. A rate needs a base of hundreds of users before it means anything. This is the base-rate mirage, and it is the single most common way a triage score inflates.

The deeper test outlasts every fixed threshold. Does the number grow when the company stops pushing on it, or only when it pushes? That distinction, demand pull versus spend, is the same one captured by the burn multiple: under 1.5x signals demand pull, above 5x signals push. You will rarely see a burn multiple in public data, but you can often see whether growth continues through a quiet week with no launch, no ad push, and no press.

The metric that grows when nobody is pushing on it is the only one worth chasing a warm intro for.

How much attention a company earns at each triage step

Match your effort to the decision. The whole point of a score is to spend the least attention that still produces a defensible call, and the data shows how little that is at each step.

The gap between the last two rows is not a contradiction, it is the shape of the pipeline. Screening is the fast filter this guide serves. Full diligence is what you do after a meeting, and it is where thoroughness pays: angels who spend fewer than 20 hours on due diligence see 2.5x higher failure rates than those who invest 40 or more. Roughly 80 percent of deal flow still arrives through network referrals, so a warm intro is the scarce resource this score is protecting.

Score a company: the procedure

Run these seven steps in order. The whole pass fits inside the fifteen-minute screening window, and it ends with a number, a bucket, and a one-line rationale you can defend later.

The signal pull in step two is where a good sourcing tool removes the most friction. Instead of hand-checking whether a company is actually staffing up in the roles that signal momentum, you can ask for the pattern directly across many companies at once.

I built Refolk to answer that kind of question in plain English across GitHub, LinkedIn, and the open web, so the ten minutes you have goes to judgement instead of tab-hopping. When a name surfaces, Refolk lets you check the founding-engineer and Head of Growth hiring pattern that separates a company shipping from one that only announced.

How this score goes wrong

Every failure mode here produces a false positive: a company that scores well and should not. Knowing the seven ways the score lies is more valuable than the score itself, because a confident wrong call costs you a warm intro you cannot get back.

Base-rate mirage. A 300 percent month-over-month jump off 10 users reads as breakout and means nothing. Check the denominator, and require a base of hundreds before you trust any rate.

Panel-traffic inflation. Estimation tools overreport small sites by roughly 94 percent, and accuracy drops sharply below about 5,000 monthly users. A surging competitor may simply be below the reliability band. Confirm the site is above roughly 5,000 monthly users before you quote the number at all.

Vanity spike. Downloads, press, and sign-ups jump on a launch and decay. Apply the test: does it still grow when the company stops pushing.

Headcount as proxy. A funded company that has stalled still shows open roles. Open roles without commit and product velocity are a false positive. Cross-check that engineering and hiring move in lockstep.

Single-signal GitHub read. Commit noise is high in AI-only repos, and star counts create outdated zombie leads. Read contributor growth and deploy cadence, never stars alone.

Checklist theater. A fully filled scorecard built on founder-supplied projections looks rigorous and proves nothing. Validate market claims against third-party data, and remember founder quality stays the highest-signal input.

Funding-announcement anchoring. Not all funded companies execute. Some raise and stall. Validate a round with post-round hiring and shipping velocity, not the headline itself.

The founding-engineer signal and where it is scarce

The founding-engineer title is a scarce, geography-concentrated signal, so the same job posting means different things in different markets. In Refolk's index, 4,387 profiles hold a Founding Engineer title at US software companies versus 604 in the UK, a 7.3x gap.

Read the gap this way. A UK startup posting for founding engineers is drawing from a pool 7.3x thinner than a US peer, so the same posting implies more intent and more urgency there. The company is fishing in a small pond on purpose, which is a stronger signal of conviction than the identical role in San Francisco, where the pool is deep. Among US founding-engineer holders in Refolk's index, San Francisco and New York are the two densest metros, and named current employers include Navier AI, Erebor, and Stellar Sleep.

The Head of Growth title is the demand-side companion signal. There are 2,118 in the US in Refolk's index, concentrated in New York, Austin, Seattle, and Los Angeles. A company hiring both a founding engineer and a Head of Growth in the same window is one where product and go-to-market velocity are moving together, which is exactly the lockstep the score is looking for.

A copy-paste scoring rubric and weights

Use this rubric verbatim, then adjust the weights to your thesis. The weighting reflects what the evidence supports: engineering velocity and traction carry the most, hiring the least, because headcount alone does not separate winners.

Founder (weight 3): 5 = repeat founder with public shipping evidence; 1 = strong resume, no public work
Engineering velocity (weight 3): 5 = contributor growth 40%+ and steady deploys; 1 = stars only, no commit trend
Traction (weight 3): 5 = grows unpushed, NRR above 106%; 1 = single launch spike, then flat
Market (weight 2): 5 = third-party demand evidence, large; 1 = founder-supplied TAM only
Hiring velocity (weight 1): 5 = founding-eng + growth roles in lockstep with product; 1 = open roles, no product velocity

Total = sum of (score x weight). Max = 60.
45-60 = pursue-now: spend a warm intro
30-44 = watch: set a signal alert, recheck in 4-6 weeks
under 30 = pass: log the name and move on

One-line rationale (required): ___

The buckets map to spend, not to opinion. Pursue-now spends a warm intro. Watch sets an alert, because GitHub acceleration leads announcements by three to six weeks, so a watched company can move to pursue-now on a fresh signal. Pass logs the name so a future signal can resurface it.

Keeping the score current

Re-run the score on watched companies on the lead-time clock, not on a calendar. Because GitHub engineering acceleration precedes a fundraise by three to six weeks and a contributor jump of 50 percent or more usually means a round just closed, a four-to-six-week recheck on watch-bucket names catches the move before the announcement makes it public. Set the alert on the contributor and deploy trend, since those are the earliest and hardest to fake.

Recalibrate the thresholds themselves as your own outcomes accumulate. Track which pursue-now calls converted to meetings that you were glad you took, and which passes you later regretted. The scorecard earns its keep by homogenizing decisions across dozens of calls, so the weights should shift toward whichever dimension most often predicted the calls you got right. Keep the two velocity signals, engineering and hiring, checked against each other every time, because the lockstep test is the one cross-check that no single-dimension threshold can replace.