Read Our Case Studies

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Synergizing Data from across the World


from data collection to statistic submission.

 

Analytical Summary

KASSANDRABLOG called up for a solution to interpret complicated and inaccessible data in a comprehensible manner. We collected data from various sources, analyzed them, and exhibited them graphically. We built visualizations of some major concerns of life including climatic change, natural resources, health, and economy. We took accessible public data and provided them with data analysis, datasets, and infographics on topical topics.  

Know our Client

KASSANDRABLOG is an Italian company birthed by a group of people working for Growing Power. Our client offers useful resources to readers, bloggers, online publications, and journalists. In a defined time, they examine inequalities in food & water consumption, infant mortality, education, and work. They make analysis accessible and understandable to the common people.  

Note their Pain Points

Finding data for world population, pollution, and COVID case altogether were the challenge pursued by KASSANDRABLOG. They needed sources providing detailed stats and reports for the same. 

Hear in their Solutions

They proposed a solution wherein we, at DataVizz, had to collect and process data from the backend.  

Discover our Specifics

We built an automated chain by creating a service wherein the data of world Covid cases, Italy Covid cases, world population, Italy population, world pollution, and Italy pollution, were automatically updated in our database. Using that database, we provided the statistics as per their requirements with graphs. We found everyday Covid details for them and created a detailed statistical report for their website. 

We also maintained their cloud, including the database, website deployment, and AWS maintenance. We built a monitoring tool by which we could monitor everything.

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Paving System for India’s Core Banking


from decoding to delivering.

 

Analytical Summary

Airtel is a third-party software; we matched the list of messages with the SMS templates, filtered out the fraudulent messages, and let the right messages pass through our security system by adding a pre-registered ID per message. Our system identified the qualified messages that must reach the audience.   

Know our Client 

Airtel or say Bharti Airtel is one of the ruling global telecommunications companies. With over 403 million subscribers, 11 crore broadband connections, 99.45% active subscriber base, Airtel is undoubtedly among the top 3 telecom operators in India.  

Note their Pain Points

The TRAI (Telecom Regulatory Authority of India), to detain fraudulent transactions took the initiative to pre-register all the promotional/ transactional SMS templates with a central portal and authorize only those transactions that matched the pre-registered template. The Core Banking Systems identified a need for a system that enables them to read the incoming transactions and qualify the message with the pre-registered ID.  

Hear in their Solutions

A system that could identify incoming messages, compare them with a pre-fed list of templates, run a Natural Language Processing, qualifies a template that matches the existing cached list of templates, and provide an ID matching with the central portal registered with the regulatory.  

Discover our Specifics

Our system could identify incoming messages, compare them with a pre-fed list of templates, run a Natural Language Processing, qualify a template that matches the existing cached list of templates, and provide an ID matching with the central portal registered with the regulatory.   

We built and delivered a robust system that processes requests for more than 80 banks, identifying templates as well as processing more than 10 million requests each month with a success ratio of over 98%. Our solution had its share of unique challenges that needed us to deploy the same microservices architecture on an on-premise setup. We decided to opt for an active load balancing setup where the Core banking Systems were load-sharing income traffic of two nodes.

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