Startups Using XML in Boston

TypeScript – With TypeScript’s compile time checking we have seen increased quality in our software with the ease of JavaScript. We also use CircleCI continuous automated building, testing and deployment which further increases our quality and development speed.

NodeJS – Our cloud-based Microservices are primarily written in NodeJS. NodeJS allows us to develop scalable and easily deployable software for our business logic and high level robot behaviors.

ROS – Our robots run on the ROS with customizations to greatly improve performance and reliability. With LIDAR, 3D depth cameras, odometry and other sensors our robots are able to move autonomously in our customer’s facilities.

OpenCV – With 256 CUDA cores on our robot you will have access to more than a teraflop of compute on our robot. Some of our highest performance algorithms utilize OpenCV to take advantage of these parallel cores.

Angular – Our graphical interfaces are built as Angular applications running on Electron giving us ease of development running on multiple platforms and fully extensible as we design new features.

Aurelia – After tinkering with a number of different front-end Javascript frameworks, we’ve moved forward with Aurelia to enable rich standards-based client-side functionality, routing and data-binding. We find that Aurelia’s test-ability and modular design work well for our small team.

ASP.NET MVC/Web API – Using these enterprise-grade frameworks and Visual Studio enables us to focus on delivering value to our customers through new web-based products and features. Documentation and IDE support for .NET and C# is incredibly rich. We also depend heavily upon continuous integration (CI), one-click builds, and Selenium automation tests to build in quality from the start.

Elasticsearch – We’re using Elasticsearch as our search platform to enable educators to construct and perform complex queries to identify and visualize data about students, such as those that might need extra attention or are at risk. Student data, especially for English Language Learners (ELLs), is incredibly nuanced and is comprised of many different properties. Elasticsearch helps us scale to support the fast searching of multiple data sets from each of our school district customers.

Python – We find that it’s one of the best technologies for data wrangling, especially when using packages such as pandas. Whether it is manipulating large datasets in MongoDB via map-reduce functions, or transforming data files containing important student data, Python is our tool of choice in this area.

Octopus Deploy – We use Octopus Deploy for our deployment automation platform. It holds a near and dear place in our hearts because it provides first-class support for Windows/.NET and is extensible, open and reliable. And it’s from Australia!