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AWS-based data and AI solutions driving subscription growth

Our client is a Pulitzer Prize-winning news platform with over 55,000 paying subscribers accounting for 45% of its revenue. With subscriptions integral to its growth strategy, the company engaged AgileEngine in the development of a performant, GenAI-ready data infrastructure for content and audience analysis. Leveraging AWS, we’ve built a strong foundation for the company’s data-driven growth, bringing speed and cost-efficiency to critical analytics workflows.

Industries

Digital media, Entertainment, Subscription

Services

Data engineering, AI engineering, DevOps

Solutions

Data pipeline, Data lake, Cloud, CI/CD, AI, Data visualization, Business intelligence

Technologies

AWS, Bedrock, Step Functions, Lambda, S3, Glue, Athena, Anthropic Claude Sonnet 3.7., BigQuery, Apache Airflow, Arc XP, Power BI, Amazon Titan, Mistal AI

Outcomes
and highlights

  • 3 weeks from project start to release for the AI-driven story classification system
  • 70% faster AI-driven content analysis compared to human performance
  • 99% cost savings on classification workflows compared to human-driven workflows
  • 93% accuracy achieved for content classification tasks thanks to AI
  • 99% of business intelligence reports migrated to an automated solution

Solutions overview

AWS-based solutions enabling granular, AI-powered content analysis

Our team introduced an AWS-based data lake and optimized the company’s ETL and ELT pipelines, creating a modern cloud-native ecosystem for content analytics. Using this ecosystem, we modernized the client’s reporting tools and developed an AI-driven content categorization tool based on AWS Bedrock and Claude Sonnet 3.7. Developed at record speed, this AI solution uses AWS Step Functions, Lambda, S3, Glue, and Athena.

Key deliverables

Data lake architecture based on AWS Lake Formation
GenAI-driven content classification and analysis system
Self-service solution enabling the analytics team and other stakeholders to create custom views, models, and dashboards with the data from the data lake
Optimization of ETL/ELT pipelines for cost savings and maintainability
Infrastructure setup for the orchestration and scheduling of data pipelines
Consolidation of KPIs from multiple APIs and data warehouses for app usage reporting
Migration of 99% of manual reports to a Power BI solution with custom workspaces, datasets, and dashboards, as well as a centralized hub with metrics and KPIs
Disaster recovery plan and safe vault solution for the Arc XP content management environment
CI/CD pipeline for moving ETL jobs and Airflow DAGs from GitHub to AWS using S3

Technologies

  • AWS, Bedrock, Step Functions, Lambda, S3, Glue, Athena, Anthropic Claude Sonnet 3.7, BigQuery, Apache Airflow, Arc XP, Power BI

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