FounderRank profile prototype

Abraham Greenman

Global #97709 United States #20358 North America #22820 verified

Public profile for Abraham Greenman, a generalist focused on practical AI systems, software tooling, product infrastructure, and professional knowledge systems; commercial scale inputs remain zero until sourced traction data is available.

Total weighted score 15 0-100

Ranking decision

Why Abraham Greenman is ranked #97709

Abraham Greenman ranks global #97709 because a 15 total weighted score is led by Proof, Durability, Reach. The rank compares scored public founder-company records across outcomes, reach, job creation, durability, and proof quality.

Proof 7.4 pts 74 score x 10% weight
Durability 3.5 pts 22 score x 16% weight
Reach 1.7 pts 12 score x 14% weight

Highest-impact raw signals

Revenue run rate $0 -> 0 signal 0 traction points, worth 0 total-score points.
Customer base 0 -> 0 signal 0 traction points, worth 0 total-score points.
Market footprint 1 markets -> 15.8 signal Feeds traction and reach, worth 2.4 total-score points.
Team size 1 team -> 7 signal Jobs score 7 is worth 0.8 total-score points.

Score = traction x 30% + efficiency x 18% + durability x 16% + reach x 14% + jobs x 12% + proof x 10%.

Score build-up

Weighted components

Each raw input is normalized to a 0-100 component score, then multiplied by that component's methodology weight.

Component Score Weight Points
TractionMeasures commercial pull through revenue run rate, segment-adjusted customer base, monthly growth, and market count. 2 30% 0.6
EfficiencyRewards revenue generated per dollar of outside capital, with a bootstrap bonus when funding raised is zero. 8 18% 1.4
DurabilityCredits operating history, repeat-company building, active companies, and exits without letting age alone automatically max the component. 22 16% 3.5
ReachCombines market footprint with current growth to estimate geographic and demand reach. 12 14% 1.7
JobsMeasures employment footprint with market-segment log normalization. 7 12% 0.8
ProofCombines public proof with source confidence so high-performing founders still need evidence quality. 74 10% 7.4

Signal math

How Abraham Greenman's data becomes the score

These rows connect the profile's unique raw data to the normalized component scores. Caps are shown where a large input exceeds the current scoring reference point.

Traction 2 component score x 30% weight = 0.6 weighted points.
Score 2 0.6 total pts
Revenue run rate $0 0 normalized score x 42% = 0 traction points. The revenue input is log-normalized against the $5B reference point. Segment: Growth software.
Score impact0 total-score points after traction's 30% weight.
Customers 0 0 normalized score x 25% = 0 traction points. The customers input is log-normalized against the 1M reference point.
Score impact0 total-score points after traction's 30% weight.
Monthly growth 0.0% 0 growth score after 4x multiplier x 18% = 0 traction points.
Score impact0 total-score points after traction's 30% weight.
Markets 1 15.8 normalized score x 15% = 2.4 traction points. The markets input is log-normalized against the 80 reference point.
Score impact0.7 total-score points after traction's 30% weight.
Efficiency 8 component score x 18% weight = 1.4 weighted points.
Score 8 1.4 total pts
Funding-adjusted revenue 0x $0 ARR divided by $250K capital base, then log-normalized to 0 before the funding bonus/cap.
Score impactEfficiency finishes at 8, adding 1.4 total-score points.
Funding raised $0 No outside funding adds a 8 point bootstrap bonus.
Score impactFunding changes the capital base used above; final efficiency impact is 1.4 total-score points.
Durability 22 component score x 16% weight = 3.5 weighted points.
Score 22 3.5 total pts
Operating years 1 1 years normalizes to 18.7, then x 70% = 13.1 durability points.
Score impactDurability no longer maxes from age alone; final score 22 adds 3.5 total-score points.
Companies built 1 38.7 normalized score x 5% = 1.9 durability points.
Score impactIncluded as repeat-founder evidence, not as an automatic cap trigger.
Active companies 1 50 normalized score x 15% = 7.5 durability points.
Score impactIncluded as continuity evidence, not as an automatic cap trigger.
Exits 0 0 normalized score x 10% = 0 durability points.
Score impactFinal durability score 22 contributes 3.5 total-score points.
Reach 12 component score x 14% weight = 1.7 weighted points.
Score 12 1.7 total pts
Markets 1 15.8 normalized score x 78% = 12.3 reach points. The markets input is log-normalized against the 80 reference point.
Score impact1.7 total-score points after reach's 14% weight.
Monthly growth 0.0% 0 growth score x 22% = 0 reach points.
Score impact0 total-score points after reach's 14% weight.
Jobs 7 component score x 12% weight = 0.8 weighted points.
Score 7 0.8 total pts
Team size 1 1 employees log-normalizes against a 20K growth software employee reference point, producing a 7 jobs score.
Score impact0.8 total-score points after jobs' 12% weight.
Proof 74 component score x 10% weight = 7.4 weighted points.
Score 74 7.4 total pts
Public proof 72 72 public proof x 72% = 51.8 component points.
Score impact5.2 total-score points after proof's 10% weight.
Source confidence 78% 78% source confidence x 28% = 21.8 component points.
Score impact2.2 total-score points after proof's 10% weight.

Evidence

How the data supports the rank

Evidence quality is shown as context and contributes through the proof component. It does not block rank inclusion while the scoring model is being calibrated.

2 sources 2 source types 79% average confidence

Interpretation

What this rank means

FounderRank is a transparent comparison of public founder-company outcomes. It is not a biography score or a subjective endorsement; it explains which measurable signals pushed this profile to its current rank and which evidence supports those signals.