Startups Using CircleCI in Boston

Via their job posts and information submitted by startups themselves, these are the Boston CircleCI startups we've found.

Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.

Tech Stack Highlights

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.

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Text-message-driven baby journal service.

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Predictive analytics for healthcare data, targeting preventable admissions, member retention, and risk-based reimbursement eligibility.

Tech Stack Highlights

Machine Learning – We build models on our Spark platform using MLlib as well as in custom Python environments where we use many of the popular Python-based machine learning libraries. We’ve invested the most in using the Pytorch library, which we use for our deep learning models.

Spark & Scala – We use a Scala-based data pipeline hosted on Spark to ingest customer data and prepare it for use in our models.

Zeppelin & Jupyter – We work with data using Zeppelin notebooks for Spark and Jupyter in our Python environments.

Automation & Infrastructure – We use CircleCI to build and deploy both our services and infrastructure. We use AWS Lambda to automate infrastructure tasks and create custom notifications and alerts to simplify our internal workflows.

AWS – We host our infrastructure on AWS. We’ve built an independently audited platform that supports working with protected health information.

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“Online support programs to improve student experience, increase retention, and engage young alumni.”

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Employee engagement platform.

Tech Stack Highlights

Ruby on Rails – We’re a Ruby on Rails application running on Heroku with PostgreSQL as our primary database and Redis available for ephemeral data. We use the Fastly CDN for our assets and Cloudinary for file management, in particular, image management. For search, we leverage Postgres’s full-text search with the pg_search gem.

Segment and Redshift – We use Segment to collect analytics about the usage of our app. We use their analytics-ruby gem and analytics.js library for back-end and front-end analytics respectively. We then leverage them to pipe the data into an Amazon Redshift data warehouse where we can analyze the data. Recently implemented Chartio to visualize that data with dashboards.

Front End Technologies – We use SASS for CSS management, Haml for HTML, and coffeescript to more cleanly write javascript. We also use Modernizr for gracefully degrading CSS features. We use Foundation Framework to make our application responsive.

CircleCI – We use continuous deployment as our deployment strategy. Every pull request is code reviewed in GitHub and deployed to an integration environment where it’s available for the reviewer, the developer, and anyone else in the organization to test and review. Additionally, we have a suite of thousands of unit tests built on MiniTest and feature tests built on Capybara, running continuously on every commit with CircleCI. Assuming it passes all our checks, manual and automated, it’s then merged into master and deployed to production by Heroku.

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