Startups Using AWS in Boston
Via their job posts and information submitted by startups themselves, these are the Boston AWS startups we've found.
Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.
Door-to-door sales reps as a service. Knoq recruits, trains, and tracks “neighborhood representatives” to sell products/services in their own neighborhoods.
Platform for tracking a customer’s engagement across all marketing channels.
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.
Computer vision for marketing and brand analytics, with search capabilities.
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.
Makers of a new cognitive status diagnostic for researchers, clinicians, and others to non-invasively diagnose cognitive impairments.
Patient experience improvement through “patient liasons” and CRM software.
Residential loan application & lender marketplace.
3D printing of metal parts.
Monitoring tech & web interface for science labs & researchers.