Our Success Stories
Know how we helped businesses reach their organizational goals
Background: An Indian e-commerce client aspired to create an all-encompassing e-commerce solution, leading to an end-to-end project for building a comprehensive data platform. The platform aimed to incorporate data warehousing for improved data management and text analysis capabilities for a recommendation system.
Solution: We crafted a well-architected solution on the AWS cloud, incorporating a machine learning model for the recommendation system. Technologies like AWS Glue, Amazon Redshift, MongoDB, and more were utilized to streamline the e-commerce data platform.
Benefits: Our solution delivered a holistic view of e-commerce operations, enabling insights from product trends to customer feedback analysis and recommendation systems, ultimately driving informed business strategies and enhancing the client's competitive edge.
Background: An Indian-based fintech company specializing in loans aimed to implement an Asset Liability Management (ALM) solution for summarizing loans and repayment data to meet RBI reporting requirements. They sought an end-to-end data engineering layer for ALM business rules and reporting.
Solution: We harnessed PySpark and Docker to create multiple data marts tailored to diverse business needs, all within OracleDB, eliminating the necessity for a separate data warehouse.
Benefits: The solution efficiently addressed their reporting requirements, offering customer-specific marts, and ensuring RBI compliance through a streamlined ALM process.
Background: An Australian advisory firm aimed to elevate its data analytics and engineering capabilities by reformatting and streamlining data from an existing database to a new structure.
Solution: We executed a comprehensive data mapping and transformation, utilizing technologies such as Domo for complete data transformations, dashboarding, and reporting.
Benefits: Our solution resulted in well-mapped data from various sources to a centralized repository, ultimately empowering the firm with advanced data analytics capabilities, enabling more informed advisory decisions driven by insights and data integrity.
Background: A US-based healthcare company serving customers nationwide with monitoring equipment, relying on SQL server systems and SSRS for reporting, aimed to create a data repository for reporting and analytics.
Solution: We crafted a well-architected solution on Azure Cloud, leveraging Azure Synapse Analytics and ADLS Gen2, along with Azure Data Factory.
Benefits: The solution reduced operational system overhead, provided enhanced flexibility in integrating diverse data sources, and facilitated the growth of KPIs and analytics capabilities, leading to improved decision-making.
Background: A US-based Fintech client, with over 40 data sources, aspired to create a central data repository and develop an Anti Money Laundering (AML) solution.
Solution: We architected and constructed an enterprise data platform for the AML product, ingesting data from 40+ sources into a data lake spanning raw, curated, and semantic layers. The implementation included a Feature Store, enhancing machine learning efficiency.
Benefits: Our solution delivered a well-structured centralized data repository, efficient AML detection via machine learning pipelines, and empowered the firm with advanced data analytics capabilities, enabling data-driven advisory decisions.
Background: A banking client sought to detect transaction anomalies within their data warehouse, prompting the creation of a specialized data mart.
Solution: We designed and constructed a robust Azure-based data mart utilizing Azure Data Factory, Azure Databricks, and Pyspark. Delta Lakes provided scalable storage, enhancing data processing capabilities.
Benefits: Our solution facilitated seamless integration of diverse data sources, accelerating accurate anomaly detection. It also optimized BI platform operations, resulting in swifter insights and decision-making.
Background: An in-house project developed an advanced web scraping system designed to extract Requests for Proposals (RFPs) from websites, connecting vendors with potential clients for enhanced business opportunities.
Solution: The solution collects, aggregates, and filters data, creating a centralized repository of potential business opportunities. It employs various Python libraries for web scraping and multiple ML models for data quality and relevance. AWS services like S3, EC2, Amazon Redshift, and ECR ensure seamless operations.
Benefits: This system transforms the way businesses identify and act on potential opportunities, offering subscribers access to a curated list of RFPs, increasing their chances of securing valuable contracts. It not only reduces time spent on manual RFP searches but also positions businesses at the forefront of potential collaborations.
