Enhancing Business Intelligence with AI-Powered Data Integration on AWS

Gazelle AI, a subsidiary of Lightcast, partnered with Matoffo to revolutionize its business intelligence platform through a secure, scalable, cloud-native data infrastructure. Facing fragmented data pipelines, inconsistent quality across vendor sources, and limited scalability, Gazelle AI needed to modernize its approach to ranking over 10 million companies by growth potential. Matoffo designed and implemented a comprehensive solution leveraging AWS, Databricks, Apache Spark, and Snowflake that transformed operations from weekly to daily data refresh cycles. Results include 85% reduction in data integration time, 20% increase in G-Score predictive accuracy, 5x increase in data processing capacity, 30% reduction in cloud infrastructure costs, and 18% increase in customer retention rate – enabling business leaders to scale operations without proportional headcount or infrastructure investment.

​​Gazelle AI, a subsidiary of Lightcast, is a leading business intelligence platform that ranks over 10 million companies based on their likelihood of expanding operations, hiring employees, or pursuing strategic initiatives. Since 2015, the organization has leveraged proprietary AI models and diverse datasets sourced from Crunchbase, Acton, DBUsa, LexisNexis, and proprietary intelligence to serve economic developers, commercial real estate brokers, and large corporations. As demand intensified for predictive business intelligence, Gazelle AI faced mounting pressure to deliver faster insights, improve prediction accuracy, and scale operations without proportional infrastructure investment. Before partnering with Matoffo, fragmented pipelines, manual reconciliation, and weekly batch updates limited the platform’s responsiveness and competitive positioning.

As Gazelle AI’s data volume expanded across multiple vendor partnerships and its client portfolio grew into new industry verticals, operational stress mounted on data integration, quality assurance, and analytical processing systems. Manual data reconciliation, inconsistent entity resolution, and limited processing capacity created a cascade of delays, quality issues, and scalability constraints that threatened the company’s market position and growth trajectory.

Key Business Challenges:

These business pressures threatened Gazelle AI’s ability to deliver accurate, timely insights while maintaining profitability and competitive positioning in an increasingly AI-driven business intelligence market where data velocity and analytical sophistication differentiate industry leaders from traditional providers.

In response to fragmented operations, quality inconsistencies, and scalability pressures, Gazelle AI established clear objectives to transform its data infrastructure into a modern, AI-ready platform.

To eliminate fragmented operations and standardize quality, Matoffo designed and implemented a cloud-native data platform using AWS, Databricks, Apache Spark, and Snowflake. The solution enables rapid innovation while ensuring resilience, compliance, and performance optimization through 6 strategic pillars: metadata-driven governance, AI-powered entity resolution, modern analytics platform, business data productization, automated classification, and structured enablement.

Before the solution:

Data integration required extensive manual reconciliation, extending refresh cycles to weekly. Entity resolution consumed significant analyst time with inconsistent results. Processing capacity forced tradeoffs between coverage and responsiveness. Lack of governance created quality issues undermining customer confidence.

After the solution:

End-to-end integration cycles dropped from weekly to daily, enabling near real-time updates. AI-driven entity resolution automatically identifies and merges duplicates across sources. Cloud-native architecture scales to process 5x the previous volume without degradation. Centralized governance provides complete lineage, quality metrics, and access controls meeting enterprise requirements.

Following successful deployment, Gazelle AI plans to extend capabilities, deepen automation, and expand market reach through 2 strategic initiatives.

The successful deployment of this secure, scalable data platform marked a transformational milestone in Gazelle AI’s evolution from a data-rich provider to an AI-driven insights platform capable of delivering superior predictions at unprecedented scale. What began as an operational response to fragmented pipelines evolved into a strategic capability, fundamentally redefining how the organization processes business intelligence.