ElectrifAi
October 31, 2022

ML-Enabled data transformation & ETL pipeline

ML-Enabled Data Transformation & ETLPipeline

When organizations embark on their AI-ML journey, the first hurdle they grapple with is how to transform unstructured data to take advantage of machine learning models. Other questions that arise are often also related to data. With staggering amounts of data everywhere, how do I get to the relevant data, and how do I normalize this data? How do I transform this data? Rule-based transformation (while accurate) is too resource-heavy, costly, and time-consuming. So, how do I get started? Fortunately, machine learning gives us many techniques to analyze, transform and categorize this data - at scale!

ElectrifAi's ML-powered ETL pipelines

ElectrifAi's ML-powered ETL pipelines help identify and transform data to solve business problems. They take in unstructured, structured, and scattered data and accelerate data transformation while reducing costs and saving time. There is automatic support for new and modified data. The solution anticipates real-world data changes and continuously learns and adapts, improving overall efficiency, and delivering outcomes with increased ROI.

In addition, the solution leverages NLP to cluster data and generate a taxonomy - adding structure to the raw data. It includes support for an agent (bot or human) where required. Finally, it provides flexibility by integrating rule-based approaches to tighten and improve accuracy.

Let us take a look at a client success story. A financial services company processing over 300 million financial transactions lost time and money dealing with transaction disputes from unrecognized merchant names. Merchants' names in bank statements are not often the same as the brands the customers may recognize, resulting in a dispute. As a result, the financial services company had to research the merchant name, address the dispute, and sometimes pay for the charge incurred due to the excess cost of processing disputes.

ElectrifAi's ML-based Merchant match solution provided scale with reasonable accuracy. The solution accommodated real-world changes by pulling in external data, including identifying non-intuitive changes in name resulting from M&A. It could integrate with the existing rule-based solution to enhance the accuracy to 95%. The financial services company experienced higher user satisfaction with higher first-call resolution, lower operating costs, and not having to pay for disputes when it was not necessary.

ElectrifAi: US’leading ML solutions provider

ElectrifAi is one of the US' leading ML solutions providers, with an extensive library of pre-built solutions enabling our clients to capture tangible benefits quickly. We work with the C-suite to understand and solve business problems through data and machine learning in diverse industries such as Higher Ed, BFSI, Life Sciences, Hospitality, Retail, Chemicals, Oil & Gas, Telecom, Insurance, etc. The insights generated by our ML solutions, such as HigherEdAi, SpendAi, ContractAi, HospitalityAi, RevCaptureAi, and InspectionAi, have helped our clients realize savings in 6-8 weeks.

Our solution does not require investment in a new platform or infrastructure. Instead, we leverage the data existing in your system to power the ML models to deliver business outcomes.

We are the last-mile solution that sits on the top to solve specific business problems and bring about savings. Contact us to learn more!

You have data, we have solutions!

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