Overview Leaderboard Methodology
Industry Report

Micro-Mobility Operators

Providers of short-distance shared mobility vehicles such as e-scooters, e-bikes, and dockless bicycles integrated into urban ride-sharing ecosystems.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 275
Total responses: 1,400
Top Brand Overall?
Lime
91/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Specialized
86

Highest score on the LBA metric.

Authority Leader?
Lime
95

Highest score on the Authority metric.

TOM Leader?
Lime
100

Highest score on the TOM metric.

Lime is the default answer in AI responses for Micro-Mobility Operators

When users ask ChatGPT, Claude, or Gemini about Micro-Mobility Operators, Lime is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Bird is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 35 Micro-Mobility Operators brands ranked

Ranked by overall AI Visibility Score (geometric mean of LBA, Authority, and TOM). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Lime
li.me
 80
95
100
91
2
Bird
bird.com
 64
56
81
67
3
Voi
voi.com
 63
29
91
55
4
Dott
ridedott.com
 50
28
89
50
5
Spin
spin.app
 60
29
65
49
6
Lyft
lyft.com
 65
27
43
42
7
Tier
tier.app
 54
13
77
38
8
Citi Bike
citibikenyc.com
 72
11
20
25
9
Bolt
bolt.eu
 45
2
57
18
10
Bay Wheels
baywheels.com
 62
0
5
4
11
Beam
ridebeam.com
 57
0
12
2
12
Neuron
neuronmobility.com
 44
0
31
2
13
Specialized
specialized.com
 86
0
4
2
14
Veo
veoride.com
 53
23
0
2
15
Whoosh
whoosh.bike
 34
0
10
2
16
Beryl
beryl.cc
 60
0
2
1
17
Capital Bikeshare
capitalbikeshare.com
 67
0
4
1
18
Divvy
getdivvy.com
 67
0
4
1
19
Donkey Republic
donkey.bike
 61
0
3
1
20
GRIN
grin.co
 32
0
1
1
21
nextbike
nextbike.net
 61
0
4
1
22
Superpedestrian
superpedestrian.com
 50
0
4
1
23
T
TREK
trekbar.co.uk
 37
0
3
1
24
Y
Yulu
yulu.bike
 49
0
5
1
25
BlueDuck
blueduck.co
 0
0
0
0
26
DROVER
joyride.city
 0
0
0
0
27
felyx
felyx.com
 50
0
0
0
28
GO Sharing
go-sharing.com
 45
0
0
0
29
Hopp
hopp.bike
 12
0
0
0
30
H
HOPR
hopr.city
 36
0
0
0
31
HumanForest
humanforest.co.uk
 48
0
0
0
32
MOL Bubi
molbubi.hu
 55
0
0
0
33
Pony
pony.com
 22
0
0
0
34
Tembici
tembici.com.br
 52
0
0
0
35
Z
Zipp Mobility
zippmobility.com
 4
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 275 shared Micro-Mobility Operators prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Geometric mean of LBA, Authority and TOM: (LBA × Authority × TOM)^(1/3). Geometric mean is used so that any single weak metric pulls the overall score down, rather than being masked by strength elsewhere.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 275 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →