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  • What Financial Business Leaders Ought To Demand From DevOps

    Unless business leaders in financial organizations are aware of what exactly to demand from the IT DevOps teams (internal or external), they do not have true control over today’s complex projects.

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  • A Full Spectrum of Our Offerings

    Every organization is at a different juncture in their data science journey. Some could be starting while others could have implemented certain pilot projects but are yet to truly benefit from their investments and truly go digital.

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  • Analytics for Energy Utilities

    Data analytics can be used within a utility the same way it's used in banking, retail, telecommunications or any other industry: assets and operations; customers; portfolio; and corporate operations

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Case Studies

  • Refining Clinical Trial Operations Through AI

    Clinical trial processes are slow, expensive, and manual intense. Artificial intelligence has the potential to disrupt every stage of the clinical trials process — from recruiting patients to tracking medication adherence, data collection and facility management.

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  • Addressing Pharmacovigilance Data Challenges Through AI

    With increasing amount of data received from individual case safety reports, pharmacovigilance industry recognizes the need to embrace technologies such as artificial intelligence (AI) at scale. Thus, significantly reducing case processing time.

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  • Payment Default Prediction using Machine Learning

    Detecting payment defaults at an early stage is very important for any financial institution dealing with lending (student loan, mortgage, credit card, etc.). It helps in identifying high-risk customers and gives issuers a better chance to counter its implications.

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  • Augmenting day-to-day operational efficiency using NLP & text mining

    80% of the data in the world is said to be unstructured by 2025, according to IDC. Handling such high volume is not the only problem, comprehending such myriad formats of data, is quite a challenge. This is where Big Data Analytics are offering new opportunities.

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  • Distribution channel

    What if you could peek into the each of your distributor warehouses? What if you could look at all of them at the same time? What if you could do this every day? AI solutions that lets you analyse and compare your distributors.

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  • Automated document classification & data extraction with AI

    Manual document processing of bank statements, invoices, patient records, etc., is time-consuming, slow, error-prone and expensive. By implementing cognitive automation, companies can achieve 95% accuracy in data extraction and reduce handling time by 60%.

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  • Predictive Maintenance

    "Smart Factories" don't always require invasive and expensive infrastructural changes. Learn how to utilise data you are already collecting on your shop floor for smarter production.

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  • Acquisition of New Clients in Banks Using Big Data

    Big Data helps firms target customers efficiently, how? By creating a customer “genomics” – customer profile - from age, occupation, channel usage, spending behavior, etc., that helps firm plan targeted promotional activity.

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  • Fintech Innovation Round Table 2018

    Quadratyx's first Fintech Innovation Round Table at The Leela, Mumbai. It was a day full of new insights on huge opportunities for businesses and practical guidance in implementing the disruptive technologies in Banking & Financial Services.

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  • Route Optimization for Aviation

    Airlines take over 12 months to plan a new route. It's a highly manual process, involves integration of multiple data points and identification of possible destinations in a trial-and-error method. How to automate this process and reduce the time taken for planning from 12 months to just a few weeks?

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  • R vs Python Why learn both

    This paper highlights the differences between the two and explains why it is beneficial to know both.

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  • The Future Of Chatbots

    In this podcast, we explore the world of chatbots. Do we want chatbots to be intelligent or useful or both? What does the future of chatbot-human interaction hold?

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