Client Case Studies


Client Case Studies

PROJECT OUTCOMES:

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Gett

Problem

Gett is the European market leader in the on-demand mobile transportation, delivery, and logistics industry. Gett is available in over 60 cities worldwide including NYC, Moscow, and London. In London, more than half of all black cabs run on Gett. Consumers benefit from pre-booking features without any surge pricing. Corporate clients benefit by reducing costs while ensuring safe, legal, and professional transportation.

Gett’s current online system allows them to accept and process delivery orders from their vendor partners. However, Gett needed to develop an API-based service that accepts orders from their partner’s disparate systems and places them into the Gett system. In addition, this service was required to be implemented as an isolated SaaS with a reporting interface, provide statistics on KPIs, handle 100,000 orders per day, and have a 99.8% uptime.

Approach

Sphere implemented an isolated SaaS with AWS that utilized the Go open source programming language that was created at Google. RabbitMQ was implemented for its reliable data delivery and asynchronous web request processing. MongoDB was chosen as the high performance data storage backend in order to accommodate the variety of APIs and data structures from different Gett vendor partners. In addition, the following Gett libraries were used: httprouter, mgo, negroni, oauth2, mgo session, graceful, rest gate, napping, and amqp.

Technologies

AWS (Amazon Web Services)

Go

MongoDB

RabbitMQ

Solution

Solution Architecture

Software Development

Functional Prototyping

DevOps Consulting

Problem

Since 2014, Delta Dental has been facing a significant digital transformation. The company’s growth necessitated the expansion of their software development and IT teams. Delta Dental required a sophisticated tool for managing all the development projects and initiatives, as well as a top-level collaboration platform for document management. To implement this solution, they first needed assistance with the migration from their legacy bug tracking system. In phase two, the senior management team required additional consulting and training on Agile Development, as well as assistance with finding a collaboration platform for document management.

Approach

Sphere Software began by performing an audit, inspecting Jira and Confluence applications and recommending improvements. Sphere then handled the full-cycle migration from Delta Dental’s legacy bug tracking system Rally ( CA Technologies ), to Atlassian’s Jira ( with a customized Atlassian Confluence solution ). Both systems were designed and configured to Delta Dental’s processes and business requirements. These improvements significantly increased the efficiency of the Software Development, IT, QA, UAT, and Business Analyst teams. Customized workflow schemes added more transparency to the full SDLC, which decreased the overhead in managing projects. Sphere also implemented custom functionalities with managers’ digital approvals. Sphere provided a set of short training demos for Jira and held personalized sessions across teams to accommodate various experience levels. Finally, Sphere created a sophisticated knowledge base to empower every Delta Dental user, from software engineers to salespeople, with Jira and Confluence best practices.

Technologies

Atlassian Confluence server

Atlassian Jira Software server

Atlassian Marketplace add-ons

Solution

Solution Architecture

Custom API Development

Front-end Redesign

Customization and Optimization

Jira Server

Confluence Server

Business Analysis

Business process modeling

Use case modeling

User stories

Agile Development with Jira Software

Business process improvement

BluVector

Problem

BluVector is a Virginia-based company dedicated to developing real-time advanced threat detection of modern malware. Not only did BluVector need help building a structure to store and record client configurations, but they wanted to update the UI / UX design of its machine learning-based analytics engine. The company was in search of a system to deploy its innovative AI antivirus sensors for its clients’ networks. Therefore, BluVector had to commission a repository customizable enough to store original configuration files and client modified versions. In addition, the storage system had to be able to pull master versions from BluVector’s update server.

Approach

To achieve its goals for obtaining a system that stores and records client configurations, BluVector turned to Sphere Software. The Sphere team proposed building on top of the industry-standard version control system Git by using Dulwich, a Python implementation of Git. Sphere created a standalone structure to manage git repositories of config files, tracking changes and pulling updates from the master server. This system was designed to trigger external scripts that update sensor machines as well. Sphere also implemented a REST HTTP service to set access rights and modify other meta-information about repositories. Swagger was used to monitor the development of this custom API lifecycle, from design and documentation to deployment and testing.

The front-end redesign of BluVector’s customer-facing website required the use of many different technologies. The Sphere team used the Bootstrap framework to make prototype designs fit perfectly on many different screen sizes and browsers. Nomad was used for Docker support and to deploy containerized applications to a cluster. The Sphere team also added new React components to the UI, which made BluVector’s website more interactive. Sphere members communicated their progress of the UI / UX redesign bi-weekly through Cisco Spark and WebEx Meetings. Overall, this project was a success because BluVector was satisfied with the API built from scratch, and the smooth interface of their website.

Technologies

Bootstrap

CentOS Linux 7

Django

Docker

Dulwich

Flask

MongoDB

Nomad

PostgreSQL

Pytest

Python

React.js

Swagger

Solution

Solution Architecture

Custom API Development

Front-end Redesign

One Transport

Problem

Gett.com is the largest provider of on-demand mobility in Europe — far surpassing Uber — across four countries, 100+ cities, and servicing 7,000 global corporations. Gett.com wanted to expand upon their current B2B services with a new and user-friendly platform, so they turned to Sphere Software to help build One Transport from the ground-up. One Transport is a web ordering and management platform that provides companies across 1,500 cities and 35 countries, access up to 200,000 different vehicles, from black taxis to executive cars.

Approach

Sphere’s team of developers brought new ideas to help build and optimize One Transport for Gett’s B2B customer base. We created a user-friendly, front-office dashboard that allows companies to view statistics showing a company’s booking trends, create employee profiles and destination points, manage account roles, choose from six different vehicle options, create travel policy rules for individual users, and much more. Our developers implemented thorough testing phases with Code Climate and CricleCI to ensure the code was written with the highest possible quality. Sphere was diligent in resolving all One Transport bug issues within 24 hours or less using Airbrake. Additionally, One Transport is only offered to Gett’s highest-rated black taxi and private hire drivers. This ensures the best-quality drivers for their B2B customers, as drivers cannot cancel their ride once a pick-up is confirmed. Sphere’s team also helped mentor new Gett employees on software development best practices, unit testing, and automated testing. Ultimately, this joint collaboration between Gett and Sphere Software lead to One Transport being nominated as a finalist for the 23rd Annual Business Travel Awards, under the category of “Best Ground Transportation Company” of 2018.

Technologies

Airbrake

Code Climate

Confluence

CricleCI

CSS3

HTML5

Jira Software

PostgreSQL

React.js

Ruby on Rails

Solution

Custom Software Development

Solution Architecture

Staff Augmentation

Staff Mentoring

Lands’ End

Problem

Lands’ End was trying to find a more effective way to extract actionable insights from terabytes of customer data. Their massive operational database lacked the functionalities required by their marketing team to produce the analytics needed to reach new customer segments.

Approach

Sphere built a ETL process to extract data from the Lands’ End operational database. A custom built web application was then created to manage all this extracted data for use by the Lands’ End marketing team.

Technologies

Hibernate

J2EE

Netezza

Struts

Solution

Solution Architecture

Custom Software Development

Team Augmentation

Extract, Transform & Load (ETL)

Rebel

Problem

Rebel is an online lending platform that is leading a change in the Brazilian financial system by offering personal loans to customers that are fast and secure with the help of smart contract technology. Rebel wanted to develop a chatbot that would not only assist their internal customer service representatives, but enhance their customer’s experience while lowering costs. However, before Rebel’s chatbot could assist customers, they needed to first enrich its knowledge-base and train the chatbot by utilizing the expertise of their customer service agents.

Approach

Sphere implemented a full-cycle chatbot development approach for Rebel through language parsing, syntax parsing, and machine learning technologies. The original XML interface of the chatbot was enhanced by using React.js and Angular.js for the web-facing application, and Spring Boot and JavaScript for the back-end. The new interface successfully removed the “rough edges” of the chatbot, and allowed it to assist both internal representatives and Rebel’s customers. Sphere and Rebel also grew the knowledge database to provide quicker and more tailored answers. For example, if a customer were to ask about the status of their loan, Rebel needed their chatbot to first ask for the loan ID and then open the back-end application to contextualize the loan ID. Machine learning and an open NLP framework was used to improve the chatbot’s ability to learn and interact with customers. Once the chatbot was completed, Sphere provided full maintenance and technical support. Through this approach, Rebel was able to achieve their business goals by reducing costs and creating a competitive advantage by adopting the technology that will play a key role in the future of customer support.

Technologies

AIML (Artificial Intelligence Markup Language)

Angular.js

KBA (Knowledge-Based Authentication)

Open NLP (Natural Language Processing) Framework

Proprietary Algorithms to Process AIML

React.js

Spring Boot

Solution

UI Development

Knowledge-Base Enrichment

Conversation Design

Custom Software Development

Maintenance & Support

Proclivity Media

Problem

Proclivity Media wanted to build a prototype of their advertising technology analytics platform. With an algorithm in mind, they needed a team with the analytics experience necessary to build a scalable front and back-end system that would allow them to establish proof of concept.

Approach

Sphere helped Proclivity establish proof of concept by designing and creating a complex solution architecture built around their proprietary technology. This required custom advanced data analytics software and database architecture that could handle the unique processing requirements.

Technologies

Clojure

Hadoop

Hive

Java

MapReduce

Pig

Solution

Solution Architecture

Custom Software Development

Cloud Analytics and Data Visualization

Team Augmentation

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