Built in Boston Startup Tech Stacks

Physician interaction data platform with profiling, predictive analytics.

Tech Stack Highlights

Flask – we migrated to Flask from Django to increase the flexibility with which we build and manage our portfolio of workflow applications. We manage our own library of plugins and cookiecutters to enable efficient setup for new team members and team members picking up new projects.

React – we moved to React from Ractive as our JS framework for its performance, component oriented architecture, and server side rendering. React raises the level of abstraction in our front-end code base, making it more predictable and allowing our engineers to focus on building functionality versus wrestling with the framework.

AWS – as a healthcare IT company, we need to maintain a very high bar for our security and privacy infrastructure, given the high-stakes nature and stringent requirements of our large hospital system customer base. Our users rely on our ProviderMatch platform to match millions of patients to the right providers every year. AWS is our core cloud platform that allows us to accomplish this at scale.

ElasticSearch – our core patient-provider matching engine is enable by multi-faceted search. We have customized ES to our domain-specific data models, query types, and end user stories. ES recently migrated away from search as a core area of innovation (in lieu of analytics), so while we are investing in ways to optimize our utilization of ES, we also continue to keep an eye on the landscape of alternatives!

Slack – Slack is core to how our team communicates and gains critical insight into how our platform is performing. The ProviderMatch platform’s services are tightly integrated into Slack, and provide notifications and real-time performance insights that allow our team to stay on top of all platform activity.

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Video hosting, sharing, optimization.

Tech Stack Highlights

Ruby on Rails – We use Rails for most of our services. It’s easy to read, easy to test, reasonably fast to learn, and opinionated in ways that we find helpful.

Elixir – We’re using Elixir for select services where high concurrency is important.

React – We are using React on new frontend features because it’s stateless paradigm makes for code that is easier to reason about and winds up with fewer bugs. It’s also nice to have a single framework across our services, so folks don’t have to learn an entirely new system every time they work on something different.

MariaDB – It seems like everyone is moving to NoSQL data stores, but we love SQL! It turns out that databases that have been around for several decades are very good at what they do — indexing, locking, transacting — and using this proven technology means we get a lot of DBMS features “for free” that NoSQL variants force you to build yourself. We do have a service at scale beyond what a single SQL database can support, and in that instance we are sharded across several database instances.

Docker – All new application servers that we build are containerized and thus entirely immutable. This eliminates an entire class of problems that arise when servers are otherwise left in an unexpected state. We never have to worry about rogue processes, old open ports, or artifacts on the file system impacting a newly-deployed set of code.

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Employee performance prediction tools for hirers / recruiters.

Tech Stack Highlights

MySQL – MySQL is used to provide the main data storage for all business critical information such as user data, jobs, candidates, assessments meta-data etc. We use NDB cluster as well as full redundancy real-time back-up server. Additionally the data is archived hourly, daily and weekly. When it comes to data security – nothing is ever too much.

MongoDB – Thousands of data points a minute are streaming to our servers in the form of user responses to pre-employment assessment answers. This data constitutes the main material for later analytics. Mongo’s Sharding technique allows us to employ multiple low cost instances to handle all this data in parallel fashion. Like MySQL data, No-SQL data is fully redundant and backed up on regular basis.

Python/R – Both Python and R are used to automate the data analytics, required for creating job-success predictions. While Python provides a much more versatile and reliable development environment (especially with modules like NumPy, Pandas, etc), R still has advantages in certain areas. Python’s rpy2 module make the two work together pretty decently.

Apache/PHP – Since our web application is a single-page app, the web service is mainly used as a REST-style backend that interacts with the browser by sending back-and-forth JSON packages. Memcached allows to maintain single state between all web instances. Other great tools like WKPDF (that is used for server-side web rendering) for creating downloadable materials, etc.

JavaScript/Web MVP – On the client we took a rather unorthodox approach of creating our own MVP framework that connects seamlessly with the backend, and makes the entire development cycle much faster. The framework that we created (ElementsJS) makes use of jQuery as well as multiple open-source jQuery plug-ins, while binding them together in a simple to use JavaScript API.

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Lesson plan collaboration platform for educators.

Tech Stack Highlights

Flask / Python – We used Flask initially to power our API, but we liked it so much that we started to move most of our critical backends to it. Flask’s flexibility allows us to swap front-ends from Handlebars (used in emailing) to ReactJS (used in rest of applications) without much hassle.
React – We use ReactJS to built most our critical applications. Most of our React components are stateless, and we have created our own flavor of Redux/Flux, as well as routing, over the years.
Node – We use Node mainly for server-side rendering. We run it as a side process for Flask to communicate to and don’t actually expose node externally.
Statamic / PHP – We use Statamic to build and serve a lot of our static content pages, including internal Q/A sites, and external public pages, such as our team, and contact pages.
AWS Lambda – We use Lambda to handle a lot of file processing, such as image rotation/resizing, PDF generation, and producing quick previews of webpages that our users might link to.
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Social media and blogger influencer identification.

Tech Stack Highlights

ElasticSearch – At the heart of it’s product, Traackr is a search engine. We leverage ElasticSearch for it’s relative simplicity of deployment while providing incredible powerful and fast search results even when used with complex queries against billions of documents.

AWS – As a SaaS business with the need flexibility to deploy new solutions or scale existing systems rapidly. AWS gives us that advantage and offer many complementary services to build advanced system quickly.

Git – “Just the code repo” right? With a distributed team and an agile process, git is very important to us. From Pull Requests for code review to CI and automatic deployments, git is always in the middle of it.

MongoDB – This NoSQL database has been a lifesaver for us. Easy to get started with it, the document model allowed us to scale quickly at the beginning when we were just discovering how to best index the unstructured (aka messy) web.

PHP/CakePHP – Good old boring PHP! Maybe not the most fancy language these days, but the incredible amount of libraries available and its sheer robustness (thousand of sites are build on it, hundred of thousand of developer are fluent in it) makes it a champion to build a production and enterprise ready solution.

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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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AI-powered tech for evaluating photos of a vehicle, detecting damage, and automating claim estimation.

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Competitive gaming contests & prizes platform.

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Sensor & computer-vision predictive analytics system for controlled-environment agriculture.

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