Case Study · Social Impact · AI Product
AdaptAI
AI-designed adaptive clothing that treats disability as a design opportunity — not a constraint.
A quick read before you dive in
An AI-powered platform that generates personalized adaptive outfit designs with hidden accessibility features — magnetic closures, side openings, seated silhouettes — built into beautiful, trendy looks.
Fashion is a form of self-expression and dignity. Excluding people with disabilities is not just a product gap — it is a social justice issue. The goal: eliminate the barrier between disability and style.
Cornell students with disabilities (100+ served through SDS), wheelchair users, people with sensory sensitivities, mobility-impaired individuals, and the artisans who serve them.
I led the end-to-end product: user research at Cornell SDS, pain-point prioritization, solution design, VLM/Python prototyping, and shipping the live web app.
Adaptive fashion is broken in four specific ways
The global adaptive fashion market is nearly invisible. Over 1 billion people with disabilities are routinely excluded from mainstream fashion. Standard clothing ignores wheelchair users, people with sensory sensitivities, limited dexterity, and unique body shapes. The 'accessible' options that exist are clinical, expensive, and strip people of their identity.
At Cornell's Student Disability Services, I saw this firsthand. Students who needed adaptive clothing either couldn't find it, couldn't afford it, or were forced to wear something that made them feel less — not more — like themselves.
High cost
Bespoke adaptive garments run $300–500+ per item, pricing out most students.
Limited stylish options
Choices are functional but rarely fashionable — no self-expression.
Poor accessibility
Standard clothing ignores mobility aids, sensory needs, and unique body shapes.
Clinical-looking designs
"Accessible" often means visibly medical — stripping users of identity.
10+ interviews. 30+ brand studies. One clear signal.
Through 10+ deep-dive interviews and 30+ brand studies, two distinct user groups emerged — each with urgent, unmet needs that mainstream fashion completely ignores.
The Person With a Disability
Students, young adults, and individuals with physical disabilities, mobility challenges, or sensory sensitivities who want to feel stylish — not just functional.
- ·Clothing that works with their mobility aid or body type
- ·Adaptive features that are invisible — not clinical
- ·Affordable options (bespoke adaptive clothing was $300–500+)
- ·To feel confident, stylish, and seen by fashion
The Artisan & Small-Scale Maker
Independent tailors, fashion makers, and small studios who want to serve the adaptive market but lack the pattern expertise and tools to do so affordably.
- ·Accessible adaptive pattern templates
- ·Reduced design iteration time
- ·A new, underserved revenue stream
- ·AI tools that don't require deep technical expertise
- ✦Users don't want "medical" — they want to look like themselves, only accommodated.
- ✦Price, not lack of desire, is the top blocker to adopting adaptive fashion.
- ✦Independent makers want to serve this market but lack pattern expertise and tools.
Cost and style first — because they unlock everything else.
From 10+ user interviews and surveys through Cornell's SDS program, pain points were mapped across urgency, frequency, and impact.
| Pain point | Urgency | Frequency | Impact | Chosen |
|---|---|---|---|---|
| High cost of adaptive clothing | High | High | High | ✓ |
| No stylish options, only clinical | Med | High | High | ✓ |
| Poor fit for mobility aids | High | High | Med | — |
| Lack of awareness / discovery | Med | Med | Med | — |
Solving affordability and attractive design creates the greatest immediate value for users — everything else compounds from there.
Three products. One inclusive supply chain.
AI Outfit Generator
User inputs their mobility needs, body shape, style preferences, and favorite colors. AI generates a complete adaptive outfit design with hidden accessibility features built in — and an image prompt to visualize it.
VLM-Powered Pattern Generation
Using Vision Language Models and Python analytics pipelines to auto-generate adaptive sewing patterns — reducing the cost and expertise barrier for independent makers and artisans.
Curated Gallery of Adaptive Designs
A browsable gallery of AI-generated adaptive looks — low-friction discovery for users who want inspiration before committing to a custom design session.
What I picked — and honestly, what it cost.
| Solution | Benefit | Limitation | Complexity |
|---|---|---|---|
| AI Outfit Generator | Scalable, deeply personalized, immediate value | Requires AI API cost management | Medium |
| VLM Pattern Generation | Reduces artisan cost by 40%, enables supply chain | Complex; requires Python pipeline | High |
| Curated Gallery | Low friction, fast to build, high engagement | Less personalized; passive experience | Low |
Generator + Gallery
Shipped together for the direct user experience — highest immediate value, lowest friction to try.
VLM Pattern Generation
Powers the artisan side quietly in the backend — expands the market without adding user complexity.
How I moved from a hunch to a shipped product.
- Step 01ResearchSDS + brand teardown→
- Step 02User insights10+ interviews synthesized→
- Step 03PrioritizationCost + style ranked highest→
- Step 04Product reqsPRD + success metrics→
- Step 05PrototypeAI generator + gallery→
- Step 06FeedbackSDS student sessions→
- Step 07IterationCopy, prompts, UX polish→
- Step 08ImpactLive app · 100+ served
The verified numbers — and what they represent.
"AdaptAI is not a fashion app. It's a statement that disability is not a design constraint — it's a design opportunity. Every person deserves to feel beautiful, confident, and seen. AI, used with empathy and intention, can finally make that possible at scale."
What AdaptAI taught me as a PM.
Accessibility is a product opportunity
Over 1B people are underserved. Treating accessibility as a first-class requirement — not a feature flag — opens entirely new markets.
Inclusive design begins with research
Assumptions about what disabled users 'need' were consistently wrong. Only direct interviews at Cornell SDS surfaced the real hierarchy of pain.
AI can serve both sides of the market
The same VLM pipeline that personalizes an outfit for a user can lower artisan costs by 40%. Good AI PM means finding these two-sided leverage points.
What I'd test next
A closed pilot with 2–3 Cornell artisans using the VLM pattern tool end-to-end, measuring true per-garment cost, turnaround time, and repurchase intent.