Submitted for Focus Area #2: Navigating Resources & Systems

Inspiration

We were inspired by three core beliefs. First, that we can bring the idea of the sharing economy to the nonprofit world. Second, we can create a secure technological platform that works in the absence of a consistent data-set. Third, that if we do our job we can push technology out of the way to allow people to be more human. If we can create a compelling way for people to help others navigate culture, education, bureaucracy, and the job-hunt then we can not only begin helping people immediately but also take in data that can serve as a stand-in for information that isn’t currently collected.

What it does

Local individuals, that we are calling guides, sign up for Lobo and select a range of things that they can help with. People can join as either a guide or an expert guide. Expert guides are vetted by local organizations and can help mentor individuals through complex tasks such as figuring out which of their skills are transferable, provide help networking, or doing complex paperwork. Regular guides help navigate everyday tasks that foreign-born individuals might struggle with, like going to the DMV, finding good restaurants and communities, or enrolling their children in school. Over time, the data we collect allows us to surface tasks in our UI that are relevant to the users in order to be more proactive in helping them. A mutual rating system embeds trust into the system, and reporting mechanisms help people feel safe and secure as they complete and help people complete tasks on our platform.

How we built it

We formed a team with diverse skill sets: a data scientist, backend developers, front-end developers, a designer, and a product/project manager. Each team member jumped right in to start designing and building the product.

From a technical standpoint, the product is built on the following technologies. Front End: Angular as the framework, served with gunicorn, using nginx as reverse proxy Backend: Python Flask framework, with Sqlalchemy as ORM on MySQL database, using Marshmallow for serialization, and an open source machine learning recommendation engine.

Challenges we ran into

There were a number of small challenges we ran into

  • Designing the right architecture
  • Scope creep
  • Getting the tech stack integrated and working
  • Working to forget processes that are used in typical enterprise development (like using branches and writing unit tests)

Accomplishments that we're proud of

Though we encountered a number of small issues along the way, the team worked swiftly and decisively to either hurdle or remove these challenges. It was impressive to see the pace that we were able to maintain.

What we learned

Though many of us work at the same company, this team is unique because we don’t often work this closely together. We also added a number of team members that are not part of Jane.ai. We learned to work really closely together, have fun with each other, and learned a few new technologies.

What's next for LOBO Internationalization is the next step for LOBO. We've created the product to scale easily and add internationalization with relative ease.

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