Who Are We?

Team Name:

Nexum Co.

Team Members:

  • Joshue Osuna
  • Cristian Soria
  • Artem Simin
  • Evelina Balika
  • Alexander Perry
  • Ahmad Alkhatib


Thomas Mirc from Mapcom

Adviser Professor

Ahmed Salem

Cristian Soria

Senior at Sacramento State University pursuing a career in Software Development with a Computer Science Bachelor's Degree. Experienced with using Object-Oriented Programming in Java and developing web applications using Angular.


Major: Computer Science, B.Sc. (Expected: May 2021)

Programming Coursework: ​Operating Systems, Data Structures and Algorithm Analysis, Computer Network & Internet, Object-Oriented Computing Graphics, Database Management

Organizations: ​Association for Computing Machinery (​ACM​), Software Developers Association (​SoDA)


Software: TypeScript, JavaScript, Java, Python, SQL, C++, C#, HTML, CSS, Git

Frameworks: ​​Angular, NodeJS, S3, Firebase, Bootstrap, ChartsJS, Pandas, Selenium


Jr. Software Engineer

Computer Presentation Systems

Feb 2020 - Current

  • Redeveloped CRM application to become more responsive using Angular framework
  • Enhanced the performance of CRM application by integrating more efficient data structures
  • Reduced latency with backend communication by supporting message passing when making HTTP calls using Observables
  • Create a Single Sign-On platform to minimize user authentication for multiple products by generating web token
  • Built backend Web API endpoints for SQL stored procedures using .NET Core/C#
  • Developed IOS and MacOS applications to deliver better quality products

IT Support

California Department of Transportation

Oct 2018 - May 2019

  • Improved desktop and laptop computer systems through installing, configuring, and procuring designated areas of support
  • Condensed overflow of inventory trackings into an organized system
  • Processed and oversaw IT equipment while providing support for over 500 employees
  • Leveraged knowledge in MySQL, Excel, Windows Imaging

Class Mentor

National Society of Black Engineers

June 2019 - August 2019

  • Developed and implemented learning styles to effectively instruct high-level material to young students
  • Increased student participation through assistance, support, and guidance
  • Served as a positive role and demonstrated leadership
  • Monitor student progress and ensure they fulfilled their responsibilities


Zoom Clone

- NodeJS Video Chat application build with Socket.io

S3 File Manager

- Angular interface for read, write, and delete commands to S3 buckets using AWS API

DnD Email Builder

- Angular Drag and Drop email editor using JavaScript DOM manipulation

Self-Executing Stock Trading Script

- Python script sends buy/sell flags to Alpaca API


Tom is currently the Executive Vice President of Mapcom who seeks to solve a problem in how Homeowners go about selling their home. The current approach Homeowners go from listing their home on the market to selling it has shown an average lost of 29% when not hiring a realtor. This shows that most Homeowners are unaware of the true value of their property.

This is where Tom's idea comes into play. The purpose of this SaaS is to prevent this market loss from happening and provide FSBO's (For Sale by Owners) the tools needed to list their property at its true value. This product would not only benefit Homeowners but also Investors and Real Estate Agents. Investors, Real Estate Agents, and Homeowners will be able to test the market without publishing an active listing by "testing the waters" and generate live leads when prelisting.


Shadow Hornet will solve problems for the following parties:


Shadow Hornet will provide Homeowners with accurate property values based off test listings. This will prevent Homeowners who wish to sell their home without a realtor from undervaluing their home. Not only will the test listing generate a value, whether it's a rental/sale, it will also provide live leads.

Real Estate Agents

When Homeowners list their property with out an Agent, not only is there a market lost for the property (due to undervalued prices), but Agents also lose the potential to profit. Shadow Hornet will allow these Agents to pitch to Homeowners before their competition even knows the home is for sale. Using algorithims to predict whether a property will be listed in a matter of weeks, Agents using Shadow Hornet will be in a major advantage.

Proposed Solution

The purpose of Shadow Hornet is not to sell homes, instead we provide users resources to be placed in the best position to either sell/rent their home. We do this by sending data through listing services like Craigslist, TurboTenant, and Facebook Marketplace to collect data from. The data includes interactions from the active test listing. The data collected allows algorithims to change the listings prices while active which allows for better calculations. After analyzing the data retrieved from the listing we are able to benefit Homeowners with estimated values and live leads that responded to the test listing.


The current resouces avaliable to Homeowners is limited when it comes to predicted property values. There are calculated estimates like Zestimate from Zillow, but the problem with that is that Zillow calculates their predicted value based off of previous data, like nearby sold homes. With Shadow Hornet, predicted values aren't based on the past, instead we focus on the current market and base it off current data, like listing interactions, as well as considering inflation and past data.


September 2020

Nexum Co. was found by following members: Joshue Osuna, Cristian Soria, Artem Simin, Evelina Balika, Alexander Perry, Ahmad Alkhatib

October 2020

Collaboration with Thomas Mirc began and the project overview was given

October - November 2020

Low and High Fidelity Prototypes designed and presented to Shadow Hornet members and adviser. After receiving feedback we were able to implement accurate visuals of the project

December 2020

Developed a test script that publishes data to listing sites for future data collection

December 2020

Become familar with required programming languages for the application

December - January 2021

Finalize ideas for MVP of Shadow Hornet to initiate development

January 2021 - May 2021

Development of Application Starts