featuredwork

Our Solution

End-to-end flight operations automation was achieved using MEAN stack. This included booking, crew planning, pilot reports, and various other reports. A modern flexible UI was implemented using Angular. It captured a large set of information from Pilot reports and enabled the dynamic display of crew scheduling.

Our Solution

We built a Machine Learning model that matches the right candidates to the right job using not just their resumes, but also social data and a number of other parameters such as GitHub activity, LinkedIn recommendations, etc. The same matching algorithm is also used to find ideal jobs for candidates.

The application was completely responsive across desktops, tablets and mobile devices. React (Virtual DOM, Partial Rendering, One Direction Data Flow etc.) was used to mitigate the latency issues with third-party data sources.

Our Solution

Our in-house design studio worked with the client’s marketing team to design an intuitive system that maximized lead conversions.  We used the latest tech stack (Angular 8 and Ionic) to develop a Progressive Web Application that was responsive, personalized and provided multilingual support (English and Spanish). A highly scalable, cloud-based, microservices solution was built on a DevOps environment that aided quick iterations to market.

Our Solution

Our application delivered instant quotes using AI, for the first time without human intervention. This reduced discounts and increased quote conversion by about 20%.  The application used historical CRM data,  decision trees, co-occurrence matrix, prediction engines, neural nets, and deep learning.

Our Solution

We built a Distributed Supercomputer where any computer on the internet running Windows, Mac or Linux could serve as a node. The nodes that are used for distributed computing are clustered using the Expectation-Maximization algorithm which buckets similar nodes into a cluster.

The assignment of a node for doing a computational task is done using a multi-arm bandit simulation. Financial settlements are done using Nebulas, a third-generation blockchain.

For managing jobs, three different schedulers were considered – Kubernetes, Nomad, and Docker Swarm. Finally, Nomad was chosen as it requires no external services for saving its state and stores it on the master node itself. Additionally, it is a single binary that acts both as the master node and the client.

Our Solution

We felt that the app was missing out on several potential use-cases and approaches that would boost their customer base and engagement levels. We brainstormed with the client and arrived at multiple use-cases; ultimately narrowing it down to the following:

• Event listing and contest feature
• Family-friendly portrayal of KissCam
• Voting on their favorite entries
• Event notifications

Our Solution

We used Conversational UX to solve the problem. Carpenters received work inquiries as chat conversations where pricing could be negotiated, orders could be accepted or rejected, and progress updates could be shared effortlessly.

Our Solution

We reduced the average report generation time of a healthcare company to 10 seconds, this a 90X performance improvement despite reducing the number of RDS instances by 20%.

To do this, we first identified the performance bottlenecks in the current system using AppDynamics. Then we re-architected the data replication strategy. We optimized encryption/decryption process without compromising on HIPAA compliance. We also made several improvements by implementing real-time data syncing using Kinesis, SQS & MySQL binlog reader.

Technical Challenges

A considerable number of dealers were based in rural locations of developing countries. They had very poor internet and mobile connectivity.

Our Solution

A mobile app was developed for the dealer in React Native while the manufacturer and financier web applications were developed in React. Isolation of component rendering from API calls was done to ensure fault-tolerance even in low-speed 2G networks. Multi-language support was provided through Internationalization (i18n).

Technical Challenges

Since the action happens suddenly in just a few minutes of a game, the server-side needed extreme performance and scalability.

Our Solution

Android and iOS apps used image optimization and resolution based photo uploads. We used microservices to scale each service individually. We also implemented auto-scaling in AWS ECS to scale the application without any downtime. Performance tuning was done for scaling the application to handle huge and sudden loads. We also built a cloud storage platform for KissCam users to store media files and scaled the application to support huge arenas to the tune of 50K simultaneous users.

Our Solution

The system we have built has collected over 5 million company names and 8 million contacts. We continue to add 10,000 data daily and we are looking to reach 1 billion data points (which is about 30 million companies) in the next 6 months.

Firm-specific data from several disparate sources is first collected. An ETL process with NLP techniques is used to normalize the data to a unified company and person profile. A recommendation algorithm predicts and recommends the companies that fulfill our targeting criteria. To get sales folks to use it, we ensured the UI was simple, powerful, and fast. We chose React + Flux. We introduced messaging-based interactions instead of forms where possible.

Our Solution

We shipped a supply chain financing platform with complex user and company on-boarding process, and trading platform to propose fund and settle transactions. On-boarding widgets were implemented using Angular.js.

Our Solution

We used Angular.js and jQuery to develop a mobile app with a responsive UI, so that field teams could also use it on a tab. OpenStreetMap (OSM) was used for mapping core data because it’s free and is used by the likes of Foursquare and Evernote. For mobile-friendly interactive maps, we chose Leaflet as it is lightweight and fast.

Our Solution

Never miss out on a striking deal, as SnapMap brings you daily deals from retail outlets, restaurants, multiplexes and more. Once you’ve made the deal and enjoyed the service, you can rate and share it with anyone. If you’re a service provider, lead users directly to your deals with SnapMap and drive sales like never before.

Our Solution

Power 20 follows a single pattern across its 14 different applications. The application has been built from ground-up to work with data from the server without any local setting. A single codebase/package is compiled and deployed with different configurations to enable 14 applications per platform for a total of 28 applications on iOS and Android.

Any new applications can be added by only including a configuration file on the server and making the configuration change on the app. Images and media explain the routines and are widely used across the applications. SDWebImage on iOS and Picassa on Android are used for efficient and intelligent media consumption to ensure the least runtime memory footprint. This, along with AFNetworking on iOS and Volley on Android, provide a robust HTTP request mechanism.

Our Solution

This had several challenges.

• Choosing the right backend server and rendering engine for the Android platform.
• Rigorous memory management techniques to ensure the best possible stream quality.
• Working on Bare Metal Android Canvas code to efficiently handle the incoming video feed.
• Intelligent switching between supported protocols (RTSP, RDP and HTTP/HTTPS) based on the connection type (LAN, WAN).
• Handling network switching scenarios and high latency use cases.
• Some video feeds had four videos stitched together. Slicing of individual videos in such cases had to be done quickly and efficiently.

After considering several streaming and content rendering engines, we finally fixed chose Android Streaming API for implementation. The API had a plethora of features and support for both Android SurfaceView and Bare Metal OpenGL rending canvas for future scaling.

We also built a robust asynchronous HTTP request component based on the Android Volley library to achieve reliable communication between EventStream REST services and the Mobile Endpoint.

Our Solution

This app gives users the options to schedule their day, share daily plans, organize events like birthday and promotion parties, invite friends, conduct polls, and chat within a group. They can also sync their entire list of Google, Yahoo, and Group Agendas calendars and keep a tab on all future events.

Our Solution

GameDay is a social network for sports enthusiasts that connects sports tournament organizers, facility owners, and players. We contributed ideas to break the initial chicken-egg problem that marketplace apps have.

Our Solution

Interesting enterprise events happening all across your IT ecosystem are converted into a live stream of events organized around Topics. Real-time analytics engines take action on these events like intelligent routing. For UX, we leveraged the principle of 20% of actions are performed 80% of the time. We enable this 80% via messaging, we built a Slack-like front-end using Angular.js and jQuery.

Our Solution

We crawled millions of records to fetch data on audience analytics, with cross-linked data from G+ & Facebook. We then used Coefficient Correlation and Linear Regression to deliver analytics for 2000+ brands, using videos that covered just 3 brands.

Our Solution

We extracted, translated and loaded the data from three different sources. We built custom models using Python, which was then applied to the crunched data. The resulting predictions were visualized using D3.js.

Our Solution

We identified medically significant events like ‘infections’, ‘antibiotics’, ‘surgery’, ‘x-ray’, ‘lab test’, and more. We then leveraged NLP to arrive at the occurrence time of significant events by identifying temporal expressions like ‘today’, ‘two weeks ago’, etc., extracted from the multiple documents and correlated them with the date of document creation. We then stored this data in a structured repository for easy retrieval to construct the timeline graph of the significant clinical events.

Our Solution

A popular short term lending portal empowers mortgage brokers to compete with banks and large retail lenders, by providing predictive technology and tools. Its CoreLogic model predicts pre-payments and loan defaults. We did the segmentation to increase the accuracy to predict delinquency of a loan using credit bureau data, customer transaction data, and customer behavior data. A GBM model was built to predict the front-end debt-to-income ratio (FEDT), which is key for predictive delinquency.  Further, we experimented with 160 variations of GBM models with Machine Learning techniques using the Caret package. Various visualizations were also done to compare the actual data set and the predicted data set.

Our Solution

We created a dictionary of medical terms and abbreviations from existing documentation for specific domains in healthcare. We then parsed the clinical narratives and used Natural Language Processing to ensure the grammatical correctness of the simplified narrative.

Our Solution

Data Science was used to track, predict, and forecast prices based on various factors. We built an application in Ruby on Rails that lets shoppers pick the best prices for products from highly popular Australian stores, such as Coles and Woolworths. The app delivered great user experience by leveraging React for the web and React Native for mobile. We also designed a search algorithm to scan product catalogs using Word2vec Deep Learning neural network and identified the cheapest product by geo-location.

Further, we simplified purchase decisions by comparing prices across brands, regions, and retail branches by predicting a product’s price (using logistic regression models) for the next 30 days. The shopping application also presented actionable insights using D3.js for visualization.

Our Solution

We analyzed the past patient history of readmissions. Then, we clustered patients based on clinical, social, and behavioral factors like associated clinical conditions, age, gender, weight, lifestyle, ethnicity, economic indicators, geography, etc. With this data, we derived a Machine Learning model to predict the risk of readmission for a patient. Finally, we tested the model on the testing data set, fine-tuned it for accuracy, and successfully ran it across new patients.

Our Solution

We set up a continuous feed of raw data logs, using AWS Kinesis from AWS CloudTrail. Data were then grouped by Time, Usertype, and Logtype into multiple batches. From these batches, variables were generated to be fed into a Machine Learning model that predicted if the data entry was an anomaly, using logistic regression.

Our Solution

The client provides a social identifier, a free web identity credential controlled by the user. We designed and implemented an algorithm (based on research from Stanford University) on Hadoop using PIG, to evaluate the authenticity of an individual’s profile attributes. We also implemented various fraud detection and prevention solutions with viral social features to enable users to leverage their social graph, to add to their assert score.