
Accelerating Amazon Opportunity Evaluation
Evaluating large Amazon catalogs required extensive product research, repeated API lookups, and manual comparison across product and market data.

Evaluating large Amazon catalogs required extensive product research, repeated API lookups, and manual comparison across product and market data.

Researchers needed scalable access to Reddit discussions without relying on slow manual collection or incomplete datasets.

Campaign performance data was fragmented across Instagram and Facebook, making influencer reporting slow, inconsistent, and difficult to analyze.

Off-market property opportunities were hidden within Facebook communities, making listing discovery and standardization difficult to scale.

Orders, manufacturing tasks, and operational data were managed across disconnected business systems, making manual coordination and reporting very difficult and time-consuming.

Collecting jobs and applying to them across dozens of hiring platforms require complex and multi-layered automation workflows.

Marketing and CRM data lived across multiple platforms, making ROI reporting difficult and time-consuming.

Event opportunities were hidden within thousands of Instagram posts and stories, making manual discovery slow, inconsistent, and difficult to scale.

Property records were spread across dozens of county portals with different formats, search methods, and anti-bot protections, making data collection difficult to scale.

Our personal data is scattered across emails, documents, cloud storage, and financial platforms, making it difficult to search and use effectively.

Managing cruise schedules, pricing, and cabin data across 60+ providers while maintaining accuracy and consistency.

PostgreSQL struggled to handle 200M+ consumer records, causing extremely long processing times and high infrastructure costs.