Executive and Research Authority
dranthonyqbowen.com
Founder-controlled environment used to develop and refine the methodology.
Applied Research · Multi-Vertical Study
The KSM™ Multi-Vertical SMB AI Visibility Study
A longitudinal applied research program evaluating whether the Knowledge Structuring Model produces measurable improvements in AI visibility across different industries, business models and entity types.
Participating organizations are assessed at baseline and at 30-, 60- and 90-day intervals using the same three-pillar methodology: Structured Extractability, Entity Salience and Citation Authority.
Study in progress · 2026–2027
Research question
The question being tested
“Can KSM™ operate as a repeatable, industry-agnostic framework for measuring and improving AI visibility across diverse SMB verticals?”
The study evaluates whether a standardized KSM™ implementation produces measurable directional improvement across organizations with different knowledge structures, audiences, geographic markets, regulatory conditions and conversion models.
Methodology
One framework. Multiple verticals. Standardized measurement.
01
Baseline
Freeze the Day 0 overall KSM™ AI Visibility Score, individual pillar scores, evidence and controlled prompt set.
02
Implement
Apply prioritized improvements to the organization’s weakest AI visibility signals while preserving a dated implementation log.
03
Reassess
Repeat the assessment at Days 30, 60 and 90 using the same core methodology.
04
Compare
Evaluate overall score movement, pillar-level changes, entity recognition, factual accuracy, citations and cross-vertical patterns.
Metrics
What the study measures
Primary outcome
Change in the total KSM™ AI Visibility Score from baseline to Day 90.
Secondary measures
- Structured Extractability score
- Entity Salience score
- Citation Authority score
- Correct entity-recognition rate
- AI-generated description accuracy
- Citation frequency and source quality
- Share of Model
- Branded and category-level visibility
- AI-referred traffic and qualified inquiries, where available
Implementation cohort
Participating organizations
PeptideDoc is the first paid client deployment, not the full scope of the study. Each implementation is reported only to the checkpoint that has been measured and documented.
Fashion and Ecommerce
sonianoel.com
The assessment moved from 33/100 to 68/100 at the second measured checkpoint. The implementation is entering Day 60; the Day 60 result is pending.
Read the Sonia Noel case studyPhysician-Led Healthcare
peptidedoc.com
The implementation moved from a 35/100 pre-launch assessment to a 62/100 Day 1 baseline. Days 30, 60 and 90 remain pending.
Read the PeptideDoc case studyAdditional verticals
- Nurse practitioner services
- Luxury real estate
- Technology SaaS
- Luxury-property rentals
- Film and television production accommodation
- Corporate and event rentals
Why it matters
Why multi-vertical validation matters
Repeatability
Can the same core methodology be implemented consistently?
Transferability
Can KSM™ operate across substantially different industries and entity types?
Practical utility
Can the assessment convert identified gaps into prioritized, measurable action for SMBs?
Study coverage
Current external coverage
MarTech Edge · October 6, 2026
KSMModel.ai, PeptideDoc Launch 90-Day AI Visibility Study
MarTech Edge covered the PeptideDoc implementation as a real-world test of whether structured digital improvements lead to measurable changes in external AI recognition and citation.
Industry coverage derived from the study announcement with additional editorial context
Read the coverageResearch integrity
Measured evidence, clearly labeled
KSMModel.ai separates observed results from planned checkpoints and unmeasured outcomes. Individual implementations may support case-study evidence, but conclusions about cross-vertical repeatability will be reserved until comparable measurement cycles are completed.
- No guarantee of AI placement or citation
- No claim of independent validation
- No client outcome published without permission
- No patient-level or personally identifiable data
- No planned result presented as an observed result
- Commercial case studies remain distinguishable from scholarly analysis
Next step
Measure your organization’s AI visibility
Establish a baseline across Structured Extractability, Entity Salience and Citation Authority and identify the signal most likely to constrain your AI visibility.