Our client’s organization was founded by members of the Berkeley AI Research Lab with families of patients affected with Dementia. The objective was to reduce the frequency and impact of falls which has been the leading cause of hospitalization in Alzheimer’s care. Their existing platform was unable to scale and monitor at a facility level. It also lacked the ability to tag the recordings of fall videos to allow the facilities team to prevent future incidents.

HashedIn suggested to perform an assessment on their code base and develop a roadmap comprising of technological initiatives which can be executed. Based on the assessment results, HashedIn proposed to re-architect and refactor their cloud services by moving it from Django and Angular 1.x. HashedIn intended to separate UI from backend with APIs and use industry best practices along with proprietary frameworks to restructure code in a short period.

Our client needed their existing AI based patient monitoring application to be refactored to make it deployable across multiple centres and have a central monitoring system that allows any potential fall to be quickly tagged and alert the facilities to quickly address the patients who are in need of immediate attention. The videos of potential fall needs to be tagged for study and preventive measure would be taken by the facilities team to ensure patient safety

The customer is a recently founded fintech company based out of the USA which is both an investment manager and a SaaS provider. They intend to build a product to assist financial analysts at investment firms to assist in trading stocks. The goal of this product is to provide customers a managed portfolio of stocks which are predicted to give the best returns for the foreseeable future. The returns for each company would be predicted by using a Machine Learning algorithm.

To provide an application that can predict stock returns for each stock against the performance of the Russell 1000 index and creating a portfolio of the predicted best-performing stocks in the index to provide insight to financial analysts to buy/sell stocks based on predicted performance.

We used TensorFlow and Keras libraries to develop an LSTM Neural network factoring in the following inputs:

Financial prediction problems are a difficult type of predictive models. The complexity comes from the dependency between the factors vary over time. To tackle this problem we need a recurrent neural network, which can learn the pattern in which the stock market moves and predict based on that.

We have more than 50 input factors which are fed into our model. LSTM model will be trained in high GPU accelerated machines. We predict a top excess return over Russell 1000. Based on the excess return prediction portfolio will be generated. The portfolio has top N (adjustable by users as per preference) companies which can overperform with respect to the index.

Our client provides customizable supply chain solutions for grocers. These include several features such as distributor onboarding, pickup locations, syncing products and customers, maps search, and order placements for both B2B and B2C customers. They wanted to optimize their supply chain from farm to fork.

Our client brings diverse food value chain knowledge combined with experience from outside the food industry implementing customer-centric internet solutions for F500 companies. They provide easy solutions for the supply chain of grocery with several features such as distributor onboarding, pickup locations, syncing products, customers, maps search, order placement for both B2B and B2C businesses.

We developed a web application, with features like tracking the origin of the product, automatic sync of available items, and automated demand forecasting. Restaurants can use it to view and order agricultural products from their nearest source and are also able to track the product to its origin. With automated sync, available items were updated at each source. Moreover, with a smart population of base products of food items, restaurants were able to automate demand planning and forecasting. HashedIn showcased its technical expertise by solving the challenges the client was facing, through various methods mentioned below.

This conglomerate is the third-largest in the Indian private sector. It has interests in viscose staple fibre, metals, cement (largest in India), viscose filament yarn, branded apparel, carbon black, chemicals, fertilisers, insulators, financial services, and telecom. This multinational has a dedicated cell, which is the data science center of the conglomerate, which is managed from their Mumbai headquarters.

HashedIn built the data pipelines and a dashboard for the plant supervisor to view and/or receive an alert (voice, SMS, email, etc.) for any employees that are breaking the stipulated safety guidelines, in near real-time. We built a dashboard for the business stakeholders to visualize the violations happening on the plant to make business decisions, accordingly. A technical dashboard was developed for system and application health anomalies and data transfer checks from one subsystem to another.

The administrator can configure the email id’s of area managers and plant-heads in the system so that they can receive detailed daily/monthly/annual reports daily. To expose this data, events, alerts to external systems for integration, which would be similar to the existing voice alert system. External systems such as turnstiles (automatic doors – for the unauthorized personnel, the doors won’t open), the plant siren (can be used for fire alert to inform all the workers to evacuate the area), etc. For the safety of the employees who were back to work amid the COVID-19 circumstances, face-mask and social distancing features were deployed, before the plants were opened.

A helmet and vest should be worn by everyone present on the plant, as there is heavy equipment operating in the area. Employees will be exposed to risks if their helmet/vest is removed while working on the site. To check compliance (whenever anyone is observed without their helmet/vest), the client wanted a system that plays a voice alert and a supervisor dashboard, that gives real-time data to the supervisor/plant-head.