Research / Product strategy / Product design

Vera, a platform that seeks fair access to housing by connecting tenants and owners anonymously and based on objective criteria

Project
Vera

Role
Product Designer

Project type
Academic

Year
2025

My role

In this project, I was involved across key project stages—research, insight synthesis, ideation, information architecture, UI design, prototyping, and validation—using AI tools to support the process.

1. Overview

Context

Online fashion has a sizing problem no one has fully solved. Customers return items, abandon carts, and stick to brands they already know — because they can't trust that something new will fit.

FitMatch is a mobile app that learns from your purchase history and fit feedback to recommend the right size for any brand, before you buy. Your size profile belongs to you, not to the store.

This is an end-to-end personal project covering the full product design process: from discovery and research through to wireframing and UI design.

AI-assisted design process

AI was integrated throughout the project as a working tool, not a shortcut. Claude supported strategic thinking, research structuring and design decisions. UX Pilot and Figma Make were explored during wireframing to accelerate iteration — outputs were always evaluated and refined based on UX criteria.

2. The problem

What the data tells us

  • 88% of online shoppers have returned an item due to incorrect sizing.

    73% have abandoned a purchase because they weren't confident about the size.

    73% limit themselves to brands they already know to avoid sizing mistakes.

    68% have no system to keep track of their sizes across brands.

Sources: PowerReviews 2024, Sendcloud 2024, Statista, iF Lastmile 2023

The core problem

Online shoppers have no reliable way to know what size to order from a brand they don't know. The tools available today are built into the retailer's platform — generic, disconnected, and doesn't account for their personal history or the real behaviour of each garment. The result: avoidable returns, abandoned carts, and an involuntary dependence on familiar brands.

Design question

How might we help online shoppers always order the right size, regardless of the brand?

2. Research

Qualitative and quantitative methods

We conducted desk research on housing legislation, discrimination studies, and the Spanish rental market context since 2020.

This was complemented by 12 interviews with tenants from diverse profiles and 2 real estate professionals, and 59 surveys of potential users to validate patterns.

Who are we designing for?

Based on interview data, we defined two User personas representing both sides of the market:

  • Marc (30) - Independent renter.

  • Miguel (49) - Inherited homeowner.

The user journey

To understand pains and gains, we created Empathy maps for both User personas, and finally mapped the full Customer journeys of both profiles.

6 insights that transformed our approach

After synthesizing research data and user stories, we identified six key insights that reshaped our understanding of the problem.

1. Emotional exhaustion is the real problem

Uncertainty, silence, and lack of feedback generate anxiety that affects mental health.

2. Discrimination is systemic

The system pushes the owner to make decisions based on intuition and fear: occupation, non-payment, or legislative changes.

3. Total lack of communication

Tenants never know why they were rejected, leading to paralyzing helplessness.

4. Economic requirements are a form of disguised discrimination

It doesn't guarantee anything, but it's the only filter, excluding people who could be excellent tenants.

5. Lack of legal information

Most tenants are unaware of their rights and sign contracts without advice because they don't know where to find clear information.

6. Discriminated groups need representation

They want someone to defend them against market distrust. They seek emotional support, not just transactional support.

What solutions are available on the market?

After understanding the problem, we conducted a competitive benchmark from which we identified two clear patterns:

  • Existing platforms in the real estate industry heavily protect owners.

  • Tenants are the most vulnerable actors, with little protection or decision power within the system.

3. Product strategy

It's not an economic problem, it's systemic

If the conclusion is that the problem is systemic, Vera's goal must be to rebuild that bridge between both sides by connecting facts, not prejudices.

Why is Vera different?

Using How Might We (HMW) questions, we defined 5 core pillars that guided every design decision:

1. Anonymity

Neutral avatar, anonymous data until the time of the visit. Protects against discrimination.

2. Objective criteria

No intuition, no bias. Verified solvency, calculated compatibility.

3. Transparency

Constant feedback, visible status, two-way communication. No more ghosting.

4. Ethical validation

Proof of solvency from the moment of registration. Fair ethical conditions from the start.

5. User-centered experience

Predictable and humane process, without unnecessary friction.

Our value proposition

We connect tenants and owners anonymously, based on objective criteria such as solvency and affinity, to make the rental process fair, efficient and free of discrimination.

Business viability

Vera's business model relies on two complementary revenue streams:

  • Owners pay a reduced commission—50% of the first month's rent (significantly lower than traditional agencies) to lower entry barriers and scale through volume, not exorbitant prices.

  • Tenants have 3 free initial visits, followed by a symbolic micropayment that filters genuine intent and reduces friction for both parties.

The result is a fair, efficient, and scalable system where ethics are embedded in the business model.

What is essential for the MVP?

To bring this to life, we couldn't work on everything. We developed a User Story Mapping for both profiles, identifying all the necessary tasks from the initial search to the contract. Then, we prioritized features using a MoSCoW framework: What is essential? What can wait?

Tenant MVP features

  1. Anonymous profile creation with acceptance of an ethical manifesto.

  2. Objective profile validation through private documentation and a solvency badge.

  3. Definition of search criteria with access only to compatible properties.

  4. Application status tracking.

  5. Visit scheduling via a shared calendar with the owner.

  6. Clear communication of the owner’s final decision.

Owner MVP features:

  1. Profile creation with manifesto acceptance and anonymous identity.

  2. Definition of the ideal tenant using objective criteria.

  3. Property listing publication.

  4. Management of compatibility and visit scheduling.

  5. Final contract management.

The 3 core flows

Given the limited time, we had to prioritize even further and choose the most essential features, resulting in an MVP focused on three key tasks that cover a full user cycle.

Owner

LIST THE PROPERTY

Tenant

CRITERIA FOR
IDEAL HOUSING

Tenant

MATCHING AND
SCHEDULING A VISIT

4. Information architecture & user flows

The user journey through the platform

We designed User flows for the 3 core features to define each phase of the process, covering the full journey from first app entry to the outcome: a list of truly compatible homes and the ability to request a visit.

Same architecture for two points of view

From the defined flows, we structured the platform’s information architecture. Having two different users meant we had to iterate the information hierarchy several times. This challenge became our ally, allowing us to simplify, unify, and eliminate tasks we initially considered essential, such as the functionality for scheduling appointments between the two users, the reassuring message, and profile validation.

Validating the structure with the user

To validate the architecture, we implemented Card sorting and Tree testing with real users:

  1. Users didn't think of "Management" or "Settings" in the abstract; they thought in concrete contexts: "Profile, where my data lives," "Properties, where I see options," "Calendar, where I see my appointments."

  2. Merged profile creation and validation into a single step.

  3. We turned "compatibility" into an actionable step: "Schedule a Visit."

  4. And removed the “ideal tenant” definition to prevent bias and discrimination.

Defining the screens

With User flows and content structure defined, we began designing low and mid-fidelity wireframes and wireflows to start defining how each part of the experience would look like.

Taking care of the user experience

During the design of the user flows, to improve the experience, we designed with cognitive biases in mind:

  • Paradox of choice: Instead of displaying 500 properties, we only showed the truly compatible ones.

  • Progressive disclosure: Instead of lengthy forms, we presented the questions step by step.

  • Jakob's Law: We prioritized the use of familiar patterns.

5. Design

The identity of ethics

We created a visual identity focused on transparency and approachability:

  • The name Vera is born from veraz (truth): a promise of transparency in an industry where trust is key. The logo symbolizes access to housing in an open and welcoming way.

  • We opted for friendly and approachable illustrations, rather than stock photography. This humanizes the interface and puts people at the center.

  • The color palette aims to convey calm and confidence, avoiding institutional coldness and visual clutter, and guiding the user through a sensitive process.

A consistent UI system

We designed a consistent system that reduces cognitive effort, builds trust, and ensures predictable behavior so users never have to guess how the platform works.

What does the user feel when using Vera?

Users arrive with doubts, fears and limited information, so we reinforced visual feedback through microcopy, confirmations and notifications:

  • Owner: Clear questions and step-by-step instructions, guiding the user through the process.

  • Tenant: Anonymous profile, prioritizing objectivity over preconceived notions.

  • Transparency: When scheduling a visit, we eliminate uncertainty. The user receives a clear message: “You are the 6th person to visit the property, and you will receive the exact location 24 h before.”

6. Prototyping

3 prototypes that complete a full cycle

With the entire foundation defined (branding, system, usability and accessibility) we moved on to designing three prototypes in Figma.

Prototype 1: Home (Tenant)

Your best match through data. The tenant home features an anonymous profile, property suggestions based on key characteristics, and direct access to the core feature: Explore.

Prototype 2: Listing a Property (Owner)

A guided, sequential listing workflow for owners: property details, photos, and document verification. The process concludes with scheduling and confirming visits, reinforcing trust in the process.

Prototype 3: Housing criteria and scheduling a visit (Tenant)

The tenant's workflow, where they define their ideal home just once. Based on these criteria, the system displays only properties that match. Each listing shows complete information and manages visits via a calendar, with a transparent and auditable process. The system connects verified owners with compatible tenants, eliminating noise, fraud, and friction.

7. Validation

Continuous testing

Validation was permanent, not a final step. Throughout the project, in addition to testing with users, we internally tested each phase.

The final User testing with 8 users validated our hypotheses but also revealed real pain points:

  • 5 out of 8 users looked for the neighborhood of the property in the Explore section... we added it.

  • 6 out of 8 users had difficulties with the calendar and selecting times... we redesigned it.

8. Road map

Vera's future

  • In this first phase, we focused only on the critical aspects: completing the MVP's features.

  • In a second phase, we will expand capabilities: including recommendations from tenants and owners, and improving decision-making and legal support by incorporating AI.

  • In a third phase, we plan to integrate public verifiers to strengthen trust and improve the business model.

9. Final thoughts

Vera taught us that the best design isn't necessarily the prettiest or the most functional. It's the one that solves real problems while respecting user dignity. When design impacts people’s lives, it becomes a tool for social change.

My design tools

Notion

Figma

Mobbin

NotebookLM

Forms

ChatGPT

Condens