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Built in Boston Startup Tech Stacks

We asked startups with Boston-based engineering teams to share 5 highlights from their tech stack, and we also gathered information about technologies used from the job posts of over 200 of the startups we've curated on BSG. We're updating this regularly as new built-in-boston startups are added, new developer roles are being hired for, and startups send in updates.

Tech stack tags cover application development, DevOps, testing, and data science technologies these startups use: programming languages, related tools, frameworks, and popular libraries. We've done our best to keep tech tags standardized and updated, but if you're a developer interested in a particular startup you should always reach out to them for the most up-to-date information.

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“Find, compare, and review doctors.” Reviews are individually human-moderated.

Tech Stack Highlights

Ruby On Rails – We use RoR for our core site implementation, enabling users to search for medical Providers and easily leave ratings & reviews. Our web pages are built using Bootstrap, HAML, SASS and Javascript technologies. We’ve built a data model system in Python that replaces ActiveRest. That model system is shared as a reusable Library by many of our other applications.

Postgres & Redis – All this is backed up by RDS instances in AWS running PostgresDB. We heavily use Redis and SOLR for data caching and queue management.

Flask/Python – The rest of our apps and services – Email systems, data analysis, internal tools – all run in Python based Flask/Flask-Restless environments.

ELK – Our logging system is run as an Elasticsearch-Logstash-Kibana stack utilizing Filebeat and Logspout for streaming the log output. From this stack we’ve also created a comprehensive Technical SEO Dashboard where we can monitor crawlers and their activity and measure the cause & effect on new site features.

DevOps – Our apps are deployed using Docker Swarm orchestration via Ansible scripts for independence from specific cloud providers. We’ve built a structure with Docker in a Blue/Green deployment methodology so there is zero downtime when releasing code updates. The system is front ended with Jenkins-CI for automated execution of Unit/Integration/Acceptance test suites.

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Recruiting sourcing as a service, plus video interviews.

Tech Stack Highlights

React.js – We use both React and Vue for our front end work. We’ve been quite happy with React but are even happier with Vue for it’s flexibility and because it’s model is more familiar.

PostgreSQL – We use Postgres as our database. Postgres is most attractive for us do to it’s strict SQL compliance as well as for Postgis, which we will be using for some of our product. We’re also using the Postgres JSON functionality for some portions of our application which are not easily modeled using a relational database.

Python – Our backend is written entirely in Python, which we find to be highly productive and elegant. We use Django as our backend framework. We get a lot of value from the ORM as well as from Django REST framework – and from the Django admin.

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Software tool suite for English-as-a-second-language teachers.

Tech Stack Highlights

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!

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KAYAK for car insurance. Quick, personalized car insurance quotes from multiple providers.

Tech Stack Highlights

Python – We’re using python for our core app, with Django/DRF powering our REST API, NLTK for NLP, and pandas running high-performance real time data analysis to calculate things like RateRank and savings estimates. We use Vagrant and Ansible for IT automation, and Jenkins and Selenium for QA automation and deployment to our AWS environment.

MariaDB – Our database runs Maria on RDS, for optimal MySQL-syntax performance. We crunch a lot of data in each query, so performance is key. Some of our queries approach 100 lines long, with multiple nested queries, dozens of joins, and layered aggregation, and we run some queries thousands of times per day.

Backbone – Backbone provided us “just enough” structure for our highly custom front-end MVC, while allowing us to build our own proprietary routing & workflow engine around it. We’re using epoxy for 2-way data-binding, and jQuery + Bootstrap plugins, in addition to dozens of proprietary UI components.

Bootstrap – Our mobile-first-responsive CSS uses Bootstrap as a baseline, but builds upon it to form a highly-customized, well-organized extensible style-guide with our own unique components and layouts. We’re using SASS class-extension, selector-nesting, and custom mixins under the hood to generate our CSS.

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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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Cybersecurity platform to detect threats coming from compromised user accounts.

Tech Stack Highlights

Apache Spark – We use Spark, Spark Streaming, and the Apache Kafka frameworks for fast in-memory compute, real-time streaming, and lambda architecture. These technologies power our cyber threat detection, remediation and visualization software.

Cassandra – Our platform relies on Apache Cassandra NoSQL database for long-term data analytics and reporting. We use Elasticsearch for real-time search and analysis and Redis for in-memory cache.

Docker – We’re built on a Docker container micro-services architecture and Ansible DevOps orchestration framework for flexible bare-metal, virtual machine & cloud deployments.

Angular.js – We use the Angular front-end framework with D3.js, and NodeJS on the backend.

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Warehouse fulfillment robots that are “2-3x faster than cart picking at half the cost of traditional automation,” from former Kiva Systems and Mimio executives.

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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Knowledgebase within your Slack to organize and share information with your teams.

Tech Stack Highlights

Draft.js & React – We recently rebuilt our text editor from the ground up on top of Draft.js. Building on Draft.js lets us create a smooth, rich editing experience that gets out of the user’s way so they can focus on sharing great content with their team. We also use Babel and Webpack to transpile and bundle up our front-end assets.

Slack – Tettra is built on top of the Slack platform. We use Slack for login and authentication and have built in notifications and slash commands. Our CEO even wrote an article about it.

PHP/Laravel – Our web application is built on the PHP web framework Laravel. Laravel comes with a ton of great building blocks including an ORM, queuing system, templating framework, and a prebuilt Vagrant box (VM) for local development to get us up and running and keep iterating quickly.

GitHub/Travis/Heroku – We use a combination of GitHub, Travis and Heroku for our continuous integration/deployment process. All pull requests get code-reviewed by a team member and have tests run automatically. Once code is merged to master, Travis runs the build and deploys to our staging environment on Heroku. We use the Heroku pipeline feature to promote staging code to production.

Intercom – At Tettra, everyone talks to customers. We use Intercom to get user feedback directly in the app, resolve bugs and inform our product process every day.

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Increasing science experiment reproducibility with a suite of tools to measure & control environmental factors.

Tech Stack Highlights

Ruby on Rails – for API and user-facing dashboard web app to manage machines and alerts and see visualizations of data. Sidekiq for background processes.

PostgresDB – but evaluating some Time Series Databases (TSDB) like influxDB.

Sass & Slim – for CSS and HTML templating.

Hosted on AWS. We use GitHub and Slack.

We are open to other languages than Ruby for the backend.

Technologies: , , , , , , ,
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Smart water cooler for less plastic waste, plus flavored drink dispensing options.

Tech Stack Highlights

At Bevi the software team is structured to have shared responsibility over all the code. Each team member works on many parts of our stack including Web Ui (React), Mobile Applications (Android), Firmware (Arduino), and Backend (Java 8). We got weekly sprints and do code reviews using git on bitbucket.

Android – The Bevi smart water coolers have an android tablet that is the main interaction point with our end users. We update and push our apps regularly and we create seasonal animations that our customers love. The android tablet also functions as an IoT device that relays all events to our backend. We often have to dig deep in the android OS to ensure the uptime of our machines.

InfluxDB – A time series database that we use to store the history of all our machines and all service data. We store our data as events in an append-only way.

Java8 with dropwizard and guava – Used to handle streams of data coming from the machines and create derived streams to compute the status of consumables and flag any abnormal behavior. The data is continuously used to optimize the working of the Bevi and the user experience. We go pretty far in using a functional programming style in java.

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