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Praxent

1 cohort  ·  0 feedback entries  ·  0 open actions
Programme record · Praxent

Quantitative data from post-programme surveys. NPS, participation, and benchmark against programme average across all cohorts attended.

Pooled NPS
too few to report
n=13 D2 · need ≥20
Cohorts attended
1
CohortCityDatesD1 nD2 nNPSProg avgGapP/Pa/D
C30San FranciscoAugust 27–28, 20261813+77+31+46
P 77%/Pa 23%/D 0%
Capability Profile
Persona mix
Developer 17%Architect 39%T.Lead 44%
AI proficiency
Applying 67%Independent 28%Frontier 6%
Seniority mix
Mgr 100%
Content depth
Too basic 11%About right 89%
Pace
Well paced 89%Too fast 11%
Relevance (mean)
4.3 / 5
D1 confidence (mean)
4.2 / 5
Confidence Δ (D1→D2)
n=13 · too few
Confidence growth by domain
Build Δ
n=13 · too few
Design Δ
n=13 · too few
Commercial Δ
n=13 · too few
Persona × Seniority
PersonaIndividual ContributorSr PractitionerMgr / Sr MgrDirectorSVP / C-Level
Architect1
Developer
Transformation Lead
Persona × AI Proficiency
PersonaLearningApplyingIndependentFrontier
Architect61
Developer21
Transformation Lead44
Satisfaction by Persona
Developer
n=2 · too few
Architect
n=5 · too few
Transformation Lead
n=6 · too few
Feedback log · Praxent

Anecdotal feedback from partner reps, stakeholders, and other sources. Each entry is sourced and benchmarked against survey data where available. Hearsay entries are flagged — weight accordingly.

No feedback logged yet. Run org_intake.py to add entries.
Action items · Praxent

Org-specific actions arising from survey data and feedback. Programme-wide actions are tracked separately in programme-actions.yaml.

No open action items.