NOEL GONZALEZ
15+ Years
of Experience UX/UI DESIGNER
15+ Years
of Experience
of Experience
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Selected Work
Rentvine
Edward Jones
SharpSpring
PwC
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EverBank
Rentvine
Rentvine
Property's Net Cash Flow Dashboard
Project Overview
The objective of the property net cash flow dashboard was to provide a high-level view of your property's net cash flow, total equity, cash-on-cash return, and ability to make contributions. Additionally, this Dashboard would allow you to compare year-over-year breakdown expenses and visualize your overall real estate investments.
The Problem
The Dashboard design addresses a crucial need in the market. It adds significant value by creating a dashboard that allows owners to view an overview of their investments, such as Net Cash Flow, Total Equity, and Cash-on-Cash Returns. We also allowed them to contribute and package all that data into a simple yet informative data visualization where they can easily see a complete breakdown of all expenses for a specific property or their entire portfolio.
Business Impact
Boosting transparency, automation, and owner autonomy—helping property managers scale operations profitably.
Project Role
As the lead designer for the Property Net Cash Flow Dashboard, I was involved in every project stage. I fostered a collaborative environment, regularly engaging with stakeholders, project managers, subject matter experts, and the development team. This ensured that the project was not just on track, but also reflected the collective expertise and vision of the team.
Goals
Rentvine's Property Net Cash Flow Dashboard goal was for owners can self-serve: track reserves, view financial breakdowns, and initiate payments—cutting down manager inquiries and accelerating actions. In addition it provides clear owner statements, balance notifications, and a payment interface—promoting trust accounting transparency.
Proposed Solution
The proposed solution for Rentvine's Property Net Cash Flow Dashboard is an exciting combination of KPIs and graphs that will empower users to visualize their net cash by month and compare year over year. The monthly expenses will provide a detailed breakdown by dollar for all expenses on a specific property. Most importantly, the Dashboard will be accessible on all devices, including desktop, tablet, and mobile, ensuring a seamless user experience and providing owners with the tools they need to make informed decisions about their investments.
Collaboration
When designing the Rentvine Property Net Cash Flow Dashboard, I collaborated with several department team members. I had multiple touchpoints with stakeholders and subject matter experts. In addition, I collaborated with front engineers to ensure the design deliverables implementation and assist with any deliverable questions or clarifications. Furthermore, I synced with back engineers to ensure the data that I was planning on surfacing on the Dashboard would be available for the front end to have access to.
Design Process
My design process started by meeting with stockholders and having a casual conversation about this Dashboard's needs and goals. After carefully evaluating and doing market research, I formulated an intake document that listed all stakeholders' requirements, including the Net Cash Flow, Total Equity, and Cash-on-Cash Return and data visualization graph for Net Cash Flow by month and monthly expense graph that will provide a breakdown of all expenses, including this month and last.
The design process continued by creating high-level wireframes and defining the information architecture. I had initial meetings with stakeholders, the project managers, and engineering to review wires and assess tech feasibility for the Dashboard and the data they would need to display.
After two rounds of design iteration, I provided a Fidelity prototype to showcase the work to the team during this call/meeting. We review all requirements in the engineering team to review once before I provide a final design solution with deliverables and a final working prototype.
Some of the challenges we faced as a team were creating custom components that did not exist in the current design system or any off-the-shelf solutions.
I started my design by creating a modal that would allow users to make contributions. The modal requirements were the ability to make a contribution, create and save payment types, and have an area where the user could enter a payment amount, agree to terms and conditions, and submit payment.
The contribution modal will have three payment types: e-check and credit or debit cards; for e-check, users can provide checking or savings accounts. The user can enter a new credit card with the required information and have the option to save this as future payments. By default, if a card payment has been saved. The next time a user wants to contribute, the payment type will be saved and displayed as the primary by default.
To contribute from a saved payment, a user would open the modal via the Make Contribution button, select the saved payment type from the Saved Payment Method, enter the amount in the required field, agree to the terms and conditions, and submit payment.
Recommendation
"Noel was a pleasure to work with during his time with Rentvine. He was able to streamline our UI/UX design process, brand and unify the designs throughout our application, which, inherently, made our developers more efficient.
Noel possesses a unique blend of technical expertise and artistic skill, allowing him to conceptualize and execute designs that revolutionized our application. He is adept at leveraging various design tools and platforms to bring concepts to life, all while ensuring seamless user experiences across different devices and platforms. One of the largest initiatives Noel accomplished while with Rentvine was redesigning our entire web-based application to become mobile responsive."
Edward Jones
SharpSpring
SharpSpring B2B CRM
Chatbot Automation Feature
Project Overview
The chatbot feature was intended to provide and improve the customer experience between the customers product and the audience. The chatbot will have the ability to capture warm leads around the clock and funnel the data accordantly as specified by the user. The Chatbot can serve as automation for QA, Sales, Customer Service and many more applications.
The Problem
The problem we faced was that we had six months to execute this feature. We did not have an existing pattern or background regarding whether to buy something, rent something, or integrate with a third party, and we needed to learn how to create this dynamic chatbot from scratch, which the user could leverage on their website or mobile phone.
Project Role
I was the UX lead designer for the Chatbot feature and worked hand-in-hand with my product manager to conduct competitive research analysis and provide recommendations regarding purchasing or building something from scratch for this feature. After determining we would make the chatbot, I had countless touchpoints with my CEO, stakeholders, design team, and engineers to learn how to leverage the SharpSpring machine learning model.
Goals
The goal was to create and design a plan for a working Chatbot for beta within six months. It would be released to a small audience to test, validate, and provide time for ideation for a better user experience.
Proposed Solution
The proposed solution was to create a three-step modal that would allow us to collect the correct amount of information that would help us to be able to specify the domain-specific area in the domain, and also allow us to understand if this was going to be for everyone a new visitor, returning visitor, or a lead in a specific pipeline or just a customer with a particular value. In the final step, the idea was to customize your chatbot. The user will be able to skin visually and apply brand colors or a logo, so it will feel native to the website or the mobile website on the phone.
The Chatbot feature release allows deeper integration into SharpSpring CRM. I designed the Chatbot feature for desktop and mobile customer experience out of the box. I utilized the current existing patterns of the CRM Visual Workflow Builder that customers are familiar with to provide a seamless new feature and increase a higher adaptivity rate.
Outcome
The Chatbot feature was realized as planned, received overwhelmingly positive support from the community, and has made a positive difference in SharpSpring's long-term strategy and product roadmap.
Recommendation
"Noel showed his true talent and UX grit was during the major new feature 'Chatbots'. He guided this project design from beginning to end, showing his ability to pivot and work within technical constraints time and again. He had endless touch points with stakeholders to ensure the designs were on track and were meeting expectations. The 6 month project was his main focus during that time but he also found time to contribute to the team on smaller initiatives and team building exercises.
When the Chatbots project was launched to the general public, it saw strong adoption rates and very low customer complaints. The Chatbots project stands out as a huge success to the company and represents a waypoint on which to set future project metrics."
PwC
PwC
Column Mapping Application
Project Overview
Designed an application that enables users to seamlessly upload headless documents, Excel sheets, or text files, and leverages Robotic Process Automation (RPA) to automatically format data and generate intelligent recommendations.
Proposed Solution
Designed an application that allows users to easily upload multiple file types and leverages Robotic Process Automation (RPA) to:
- Automatically clean and format raw data
- Standardize file structures for consistency
- Provide intelligent recommendations based on the processed data
The intuitive interface guides users through the upload process and gives real-time feedback, making complex automation accessible to non-technical users.
Impact
- Significant Time Savings — Automates repetitive data-mapping tasks, reducing processing time by up to 70%.
- Enhanced Data Quality and Consistency — Reduces manual errors, ensuring reliable outputs that are ready for downstream systems.
- Increased Scalability — Allows teams to handle large datasets and frequent uploads without increasing headcount.
- Boosted User Productivity — Frees users from manual formatting so they can focus on analysis and decision-making.
- Higher Adoption and Satisfaction — The clean, intuitive interface encourages wider adoption among both technical and non-technical users.
Design Challenges
Displaying incomplete or inconsistent data: One challenge was how to display partial or missing data without confusing users or causing them to make incorrect assumptions. We addressed this with clear placeholders, distinct status icons, and progressive disclosure to reveal details only when needed.
Building trust in automation recommendations: Users needed to understand why a field was recommended for mapping and still feel in control of the process. To address this, we included transparent explanation tooltips, color-coded statuses (mapped, suggested, unmapped), and simple approve/reject actions to reinforce confidence in the system.
Visual clarity with complex datasets: Managing large volumes of data required a layout that could accommodate multiple data types while remaining easy to scan. The split-screen approach (configuration on the left, data visualization on the right) allowed users to focus on one context at a time while still seeing how their actions impacted the dataset in real-time.
Automating Data Uploads and Validation for Greater Efficiency
Benefits
- Streamlined uploads: Supports text, Excel, and headless documents with configurable separators and formats.
- Automated mapping: RPA suggests column mappings based on prior data sets.
- Real-time validation: Flags invalid data, unmapped fields, and inconsistencies before processing.
- User-friendly interface: Clear indicators (mapped/unmapped/suggested) and previews reduce user errors.
- Global flexibility: Supports different regional data formats and file structures.
Impact (with metrics)
- 70% reduction in processing time by eliminating manual data formatting.
- Near-zero data errors through automated validation and standardized mapping.
- Scalable for large datasets without additional staffing.
- Boosted user adoption among both technical and non-technical users.
- Improved confidence in data integrity, accelerating downstream decision-making.
To further enhance usability, KPI indicators were integrated at the top of the interface. These KPIs surface critical insights—such as unmapped fields, invalid data points, or suggested mappings—helping users quickly identify and resolve issues during the import process.