Overview Leaderboard Methodology
Industry Report

Specialized Engineering Bootcamps

Bootcamps focused on specific technical tracks such as data science, UX/UI, cybersecurity, or mobile development that provide targeted, job-ready skills.

Brands tracked: 39
Brands analyzed: 39
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 299
Total responses: 1,540
Top Brand Overall?
Springboard
67/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
GitHub
100

Highest score on the LBA metric.

Authority Leader?
Springboard
43

Highest score on the Authority metric.

TOM Leader?
Springboard
90

Highest score on the TOM metric.

Specialized Engineering Bootcamps has no dominant brand in AI responses

Springboard is the top-ranked brand here, but its overall score of 67 reflects a competitive field rather than a dominant player. AI assistants answer category questions about Specialized Engineering Bootcamps with varied recommendations from query to query - users get a different set of suggestions each time. This is the kind of category where mid-tier brands still have room to move up.

Brand Leaderboard All 39 Specialized Engineering Bootcamps 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
Springboard
springboard.com
 78
43
90
67
2
General Assembly
generalassemb.ly
 75
36
64
56
3
Fullstack Academy
fullstackacademy.com
 74
26
43
44
4
Flatiron School
flatironschool.com
 74
13
48
36
5
App Academy
appacademy.io
 68
13
39
33
6
Hack Reactor
hackreactor.com
 76
15
26
31
7
CareerFoundry
careerfoundry.com
 61
3
29
17
8
Coursera
coursera.org
 72
7
6
14
9
edX
edx.org
 65
4
5
11
10
Thinkful
thinkful.com
 70
1
22
10
11
Codesmith
codesmith.io
 77
1
13
9
12
BrainStation
brainstation.io
 61
0
27
3
13
A Cloud Guru
acloudguru.com
 77
0
14
2
14
DataCamp
datacamp.com
 78
0
13
2
15
DeepLearning.AI
deeplearning.ai
 82
0
11
2
16
Designlab
designlab.com
 37
0
13
2
17
Evolve Security Academy
evolvesecurityacademy.com
 36
0
17
2
18
KodeKloud
kodekloud.com
 78
0
12
2
19
Le Wagon
lewagon.com
 82
0
15
2
20
Metis
thisismetis.com
 27
0
20
2
21
Nucamp
nucamp.co
 53
0
8
2
22
NYC Data Science Academy
nycdatascience.com
 60
0
12
2
23
TripleTen
tripleten.com
 60
0
6
2
24
Udacity
udacity.com
 84
0
13
2
25
4Geeks Academy
4geeksacademy.com
 49
0
0
1
26
Codeworks
codeworks.me
 35
0
1
1
27
Coding Dojo
codingdojo.com
 65
0
3
1
28
GitHub
github.com
 100
1
0
1
29
Ironhack
ironhack.com
 66
0
4
1
30
Lighthouse Labs
lighthouselabs.ca
 64
0
0
1
31
S
SecureSet Academy
secureset.com
 37
0
4
1
32
Coder Academy
coderacademy.edu.au
 47
0
0
0
33
C
Constructor Academy
constructoracademy.com
 26
0
0
0
34
Devmountain
devmountain.com
 57
0
0
0
35
Jigsaw Labs
jigsawlabs.io
 0
0
0
0
36
L
LearningFuze
learningfuze.com
 39
0
0
0
37
Sabio
sabio.la
 42
0
0
0
38
Turing College
turingcollege.com
 37
0
0
0
39
WBS CODING SCHOOL
wbscodingschool.com
 43
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 299 shared Specialized Engineering Bootcamps 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 299 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 →