Expert Advisor Directory
A rules-based system that turned 158 inconsistent advisor profiles into a directory GSB founders can actually search.
SYSTEMS DESIGN · INFORMATION ARCHITECTURE · AI WORKFLOWS
Role
Center for Entrepreneurial Studies Intern @ Stanford GSB
Collaborators
Associate Director of Stanford Venture Studio
Timeline
Summer 2026
Tools
Airtable, Clay, Claude
158
Advisor profiles standardized
10+ 3
Tags per profile
~40 hrs 30 min
To standardize every profile
01 — Problem
Ten tags, zero signal
The Expert Advisor List is a vetted, self-serve directory of operators, investors, and service providers who advise Stanford founders.
Advisors chose their own industry tags on an intake form, and many over-tagged, some with 10 or more. Every bio was also written differently, so students got long, noisy results and couldn't compare advisors at a glance.
Goal
Make the list more usable for the students who rely on it, with every profile tagged and written the same way.
02 — Research
Interviews with student users
I interviewed 3 students who had recently used the list, and one tested the redesigned interface. I asked why they used it, how relevant their results were, which filters helped, what was missing, and what else they used to find experts.
01
Students search by industry and keywords
Industry tags carry the most weight, so they had to be accurate.
02
The list is valued for personal connections
Students use it to find people well connected in a new space, and see higher response rates than cold outreach.
03
Students are already using AI to search it
The old filters were hard enough to use that students worked around them.
03 — Information Architecture
Tagging rules and streamlined bios
With 158 profiles, bespoke fixes were out of the question. I limited every record to three fields, Industry, Role, and Type of Venture, and defined rules for each.
01
Why it matters
The GSB's educational philosophy on entrepreneurship
"Why should I care?"
02
MBA1 Foundations
How the core curriculum already serves founders
"What am I already getting?"
03
Course selection
5 layers and 2 impact tracks
"What should I take?"
04
Student pathways
3 pathways across MBA1, summer, MBA2, and beyond
"What does doing this look like for me?"
03 — Information Architecture
Tagging rules and streamlined bios
With 158 profiles, bespoke fixes were out of the question. I limited every record to three fields, Industry, Role, and Type of Venture, and defined rules for each.
01
Why it matters
The GSB's educational philosophy on entrepreneurship
"Why should I care?"
02
MBA1 Foundations
How the core curriculum already serves founders
"What am I already getting?"
03
Course selection
5 layers and 2 impact tracks
"What should I take?"
04
Student pathways
3 pathways across MBA1, summer, MBA2, and beyond
"What does doing this look like for me?"
02 — Research
AI automation system
The Center's enterprise workspace doesn't allow Claude to access Airtable, so I worked around it: I duplicated the base, had Claude apply the rules to the copy, and reviewed the results before they went live.
Audit
Reviewed 40+ Center websites, newsletters, and platforms.
2
Staff interviews
Every staff member reported students lacking resource awareness.
3
Chatbot prototype
Built an LLM assistant designed to tailor resources to individual students.
4
Missing framework
Realized that advisors had no shared model for how courses fit together.
5
Course stacking
Designed the framework: 5 layers, 3 student pathways, 2 impact tracks.
04 — Final Frameworks
Course Stacking
Think of each category of electives as a layer within a stack. Stacking courses from each layer is a strategy for building your education around entrepreneurship and venture exploration.
Why: students take about six electives out of 50+, so grouping courses by what they teach helps them spread their choices across different kinds of learning instead of doubling up on one.
Startup Fundamentals
Case-based classes following the arc of a startup and lessons in entrepreneurial management
Functional
Round out your skill set by learning critical functions through the perspective of entrepreneurial contexts
Industry-Specific
Dive into innovation within industries of interest, exploring nuances and deepening industry connections
Experiential
Hands-on learning to identify and evaluate the viability of a new venture
Useful for Entrepreneurs
GSB courses that provide valuable skills that are not exclusively entrepreneurial
Impact: Ecopreneurship
Solve climate and sustainability challenges through entrepreneurship
Impact: Social Innovation
Impact the world through founding mission-oriented organizations
Three student pathways
Why pathways: 70% of MBA students arrive interested in entrepreneurship, but at very different levels of conviction, and most GSB founders launch 3+ years after graduating. Each pathway maps courses and programs across both MBA years, the summer, and post-graduation, so every student can see a plan that fits where they are.
High-Conviction Entrepreneur
High conviction entrepreneurs come to the GSB with the purpose of starting a business and pursue dedicated, entrepreneurial learning.
Strategic Tester
Intentionally testing whether an entrepreneurial career – as a founder or early joiner – is the right fit. They come to the GSB ready to build an entrepreneurial mentality and skill set for potential future use.
Open-Minded Explorer
Open-minded to exploring different domains and career paths, often landing on entrepreneurship after discovering an idea or meeting a team through other classes, work experiences, or co-curricular activities.
06 — Outcome
Now in full operational use
Course stacking now anchors the Center's student presentations and has been shared with academic advisors across the GSB as the standard for entrepreneurial education guidance. It also provides any future AI advising projects the shared framework it was missing.
Lesson
Look one level deeper
The right product is often one level deeper than the one you set out to build. By setting our original idea aside and listening to what students and advisors actually needed, I delivered something more impactful than if I'd stuck with the chatbot.
Lesson
AI is only as good as the systems underneath it
My chatbot could surface resources, but without a shared framework, it had no consistent way to decide what to recommend. Building that foundation first means any future AI tool can advise the way the Center actually wants students to learn.
How I'd measure success
Time for students to answer key questions
Because the framework’s impact plays out over a full MBA, I’d measure it through advising itself: whether advisors give consistent recommendations to similar students, whether more students build a balanced stack across layers, and whether more apply to experiential courses earlier.