Need a review of your current technology stack? Want help determining the best technologies to implement in your application? Need experienced developers in Ruby on Rails, React, Salesforce, Data Science, and Machine Learning? We've got you covered – reach out today.
We built a multi job board platform to support a range of tech related job boards. Starting with RubyNow - the first Ruby on Rails specific Job board founded in 2006, the platform now supports multiple sites with a common backend infrastructure. We use chef scripts to ensure fast, repeatable deployments on AWS, linode and Heroku.
Built from the ground up to support an outbound approach to recruitment, this technology incorporates:
- a chrome extension
- auto emailing and template engine
- data analytics
- machine learning candidate recommender
We built VidFitness using React for the frontend and a Nodejs backend hosted on Heroku. With React we could build an application that would operate directly on the web as well as be embedded in other applications. This project leverages AWS infrastructure for video transcription and asset management.
Electron is a native client application framework that can run on Windows, Mac and Linux. By using this platform it was possible to write all the application code in a mix of common javascript and platform specific C++ code to support bluetooth and ANT+ protocols for communications with specific devices.
We worked closely with Yale School of Management's Program on Financial Stability to build out their Power BI application that collects economic policy responses from official government websites around the world. We extended their system to interactively display multiple forms of data interactively on their public facing dashboard.
We have a long history working with machine learning and AI. Dr Stephen Robinson was originally invited to the US to research autonomous robotics using neural networks at the Center for System's Science at Yale University. Since then we've worked with python and the state of the art open source libraries to clean data, meta-label information and use a range of algorithms including Random Forrests and LSTMs to offer predictions. We've applied this knowledge in modeling financial instruments to advanced visual image processing.
