Fit scores and costs are prototype estimates—not rankings or admission probabilities. Verify details with official sources.
Maya Putri's Atlas Field
Evidence-led. Future-oriented.
82% confidence
Career ambitionBuild an AI company that expands access to education
Career directionHigh clarity
Academic readinessStrong builder
Learning environmentProject-based
Geographic openness2 regions
Financial range$35k / year
Prioritize rigorous CS programs with repeated work-integrated learning, then use the next four months to prove product impact and technical depth.
Signal model
Current strengths and readiness
Founder potential88
High agency and repeated product shipping.
Innovation86
Finds useful problems and turns them into experiments.
Leadership79
Early evidence through club and team ownership.
Technical depth74
Strong builder signal; needs more rigorous validation.
Global readiness71
English readiness is solid, funding plan is unresolved.
Research readiness54
No formal research or technical writing evidence yet.
Balanced view
Your signal shape
Scores represent evidence strength in this prototype—not inherent ability or psychological traits.
Evidence → inference → decision
How your answers change the strategy
What you told usWhat Atlas inferredEffect on recommendations
Build an AI company that expands access to educationFuture direction sets the capability modelPrioritize programs that support the intended identity
Project-based + EntrepreneurshipPreferred learning environmentReweight curriculum and delivery fit
Canada + USA; $35k / yearGeography and affordability constraintsKeep fit separate from financial viability
Leads a school coding club + Shipped two web products + Built a tutoring marketplace + No formal research yetCurrent evidence affects readiness and confidenceCreate evidence gaps without reducing long-term fit
Reasoning trace
From ambition to education path
01
goalEducation-access founder
Build technology that makes high-quality learning more accessible.
02
careerAI product founder
Combine technical judgment, product discovery, and company-building.
03
skillML systems + product
Build reliable AI products and discover problems worth solving.
04
majorComputer Science / AI
Prioritize strong foundations with room for entrepreneurship.
05
programWaterloo Computer Science
A rigorous, applied path with sustained co-op experience.
06
universityUniversity of Waterloo
A builder-heavy ecosystem with deep industry connection.
07
outcomeTechnical founder readiness
Graduate with shipped work, technical depth, and a strong network.