Score methodology — Product Score (algorithm: product_v1)
How The Product Score Is Computed
The Product Score is a 0–100 composite that only counts what we can actually verify — deployment footprint, spec data, funding, and media coverage do most of the work. Missing data is excluded — never counted as zero — and the score is normalized against whichever signals are present. The result is a fairer ranking that doesn't punish a real product for the gaps in our data.
Recomputed nightly. Sourced entirely from public data. Nothing about the ranking is purchasable.
TL;DR
- Skip-and-normalize. Missing data is skipped, never zero-filled.
- Coverage multiplier. More verified data points = higher confidence.
- Rankings can't be bought. No tier, subscription, or partnership affects your Product Score.
What Robolist.ai Is — And Isn't
Robolist.ai is a public leaderboard, not a marketplace. We rank the world's robots by an objective, uncapped Product Score derived from structured spec data — payload, reach, runtime, price, launch date, and category-specific attributes. Rankings cannot be bought, claimed, or unlocked by any subscription tier. There is no "partner" path to a higher score.
We are not a procurement platform. We do not host buyer reviews, broker quotes, or take a cut of transactions. Companies cannot pay to be ranked higher; they can only pay to present themselves more completely on a profile page that every other company already has access to in a free form.
Think CoinMarketCap for robots — an independent, methodology-driven source of truth — not G2, not Thomasnet, not Alibaba. Every robot in our database is ranked by the same formula, whether the company is a Fortune 500 incumbent or a two-person startup that has never heard of us.
Robolist.ai exposes two distinct numbers. Conflating them would let our data coverage masquerade as a quality signal — which it isn't.
Product Score (public)
Quality signal
What we've verified about the product: deployment footprint, spec data, manufacturer funding, media coverage, market availability, and company maturity. Drives the leaderboard. Shown on every public robot and company page.
Profile Completeness (dashboard)
Coverage signal
How much of a robot's page is filled in for its category. A motivational metric for owners; never used to rank robots and never shown on public pages. Visible only inside the company dashboard.
Each factor below either contributes a number 0–100, or is marked absent. The normalized score is the weighted average over present factors only — present weights are renormalized so they always sum to 1. A robot with three strong signals can earn a normalized 70+ even though the other factors are missing.
This replaces the prior approach where missing fields counted as 0, which let our coverage gaps masquerade as quality problems and produced unfairly low scores for legitimate commercial robots. A factor only earns coverage credit (see below) if its computed value is greater than 20 — so filling every field with garbage data does not register as comprehensive.
A score built from one signal is less confident than a score built from seven. To stay epistemically honest, the normalized score is scaled by (signalsPresent / 7) ^ 0.3. The curve is gentle — a robot with five solid signals keeps 92% of its normalized score — but a single-signal listing cannot pass for a comprehensive evaluation.
| Signals present | Multiplier | Raw 100 → |
|---|---|---|
| 1 | 0.69 | 69 |
| 2 | 0.78 | 78 |
| 3 | 0.84 | 84 |
| 5 | 0.92 | 92 |
| 7 | 1.00 | 100 |
This is data confidence, not platform relationship. An unclaimed company with seven strong public signals still hits 100. A claimed partner with one weak signal scores ~69. The math does not know — and does not care — whether a company has claimed their page or has any relationship with Robolist.
7 factors. Default weights — used as a baseline for every category. See category tuning below for adjustments per robot type.
| Factor | Default weight | Data source |
|---|---|---|
| Deployment footprint | 35% | Press releases, case studies, customer announcements |
| Company financial health | 20% | Crunchbase, public filings, manual verification |
| Spec completeness | 20% | Manufacturer product pages and data sheets |
| Media mentions (trailing 12 months) | 10% | Industry publications, news aggregators |
| User reviews | 5% | Verified user reviews on Robolist.ai |
| Market availability | 5% | Deployment records by country |
| Company maturity | 3% | Company founding year (Crunchbase, public filings) |
A humanoid and an industrial arm don't share the same buyer criteria. We tune the factor weights per category so each segment is judged by what its buyers actually look at first. The three highest-volume categories on the platform have custom weights; every other category uses the universal defaults above.
| Category | Tuned factors | Why |
|---|---|---|
| industrial arm |
| Integrators compare arms on reach, payload, and repeatability — spec data is load-bearing here. |
| humanoid |
| Mainstream press is a real signal for humanoids; spec sheets are still maturing across the segment. |
| cobot |
| Cobots are bought on safety and ease-of-use as much as raw mechanical specs. |
Tuned weights are renormalized so the vector still sums to 100% across all seven factors.
Profile Completeness measures how much of a robot's page is filled in for its category. A humanoid's page is “complete” when humanoid-relevant fields (height, payload, battery, onboard compute, hand DOF, …) are populated; an industrial arm's page is “complete” when its core fields (reach, payload, repeatability, controller, IP rating, …) are populated. Universal fields — description, hero image, year, price, availability — count for every category.
It does not affect the Product Score. Two pages with identical Product Scores can have very different completeness percentages. Completeness exists so manufacturers know where to invest their data effort; it is shown only inside the dashboard, never on a public page or the leaderboard.
- Missing signals are excluded from the score, never counted as zero. Sparse pages are not punished for our coverage gaps.
- The Product Score is uncapped. Claim status, verification tier, and any business relationship with Robolist have zero effect on the score — trust is signaled separately via badges.
- Data loses 2% of its weight per month after 12 months without re-verification.
- Every contributing fact carries a source URL and a scrape timestamp.
- Leaderboard ties break on number of present signals first, name second — a 3-signal real product always ranks above a 1-signal listing at the same numeric score.
- Disputes are handled at support@robolist.ai within 48 hours.
The Product Score is permanently decoupled from commercial relationships. Premium subscriptions and sponsored placements affect visibility only — sponsored slots on category pages are clearly labeled as “Sponsored” and are the only commercial surface on the site.
No payment, claim, or verification of any kind can move a robot's Product Score. Scores are computed from public deployment data, spec completeness, company financials, media coverage, market availability, and company maturity — never from revenue relationships. See our Transparency page for active sponsorships.
Trust signals are separate from the score
The Product Score reflects product quality based on verified data. It is never influenced by the company's relationship with Robolist. Trust is a separate signal, shown next to the score, with its own badge family across identity, subscription, cohort, and spec axes. See our badge system →
Algorithm product_v1 replaces the earlier v1 (4-factor) and v2 (8-factor, zero-fill) scoring approaches. Tier-based score caps and the verification factor were removed in 2026-05 to permanently decouple score from claim status; the coverage multiplier added at the same time keeps single-signal listings honest. Older snapshots are retained for history; the leaderboard reads each robot's most recent snapshot.