A Brand-Protection Platform, Modernized on AWS — From Monolith to Lambda Microservices

IP Shark had already moved to AWS in response to rapid growth. The platform crawls online marketplaces — millions of listings — to detect counterfeits and help rights holders enforce against them. More growth meant more crawls, more data, more customers. AWS was supposed to handle it.

It didn’t. Throwing cloud resources at the problem wasn’t resolving the bottlenecks. Response times degraded. Costs climbed. The team didn’t have deep cloud expertise in-house, and the architecture that had been lifted into AWS was the same architecture that had been struggling before.

They brought us in to figure out why.

The relationship started small and grew deliberately.

Embedded engineering. A single Polymath engineer joined IP Shark’s team to add marketplace crawlers, strengthen data validation, and improve automation. The goal was to earn trust by shipping.

DevOps focus. Fixed-scope DevOps work: making the database accessible for development, wiring up application monitoring, scripting the infrastructure so nothing was manual, and setting up storage for crawl artifacts.

Full delivery. IP Shark handed over IT operations entirely. Our team took on product development, testing, deployments, and production support, with Polymath leadership running the technical direction. The shape of the engagement moved from “help us” to “own this.”

Managed operations. After the modernization stabilized, the engagement shifted to an ongoing SysOps retainer — uptime, incident response, AWS health scanning, crawl anomaly management.

The diagnosis had three parts. None of them were novel. All three had to be fixed before cloud resources could do their job.

Data model optimization. The data layer was the throttle point. We restructured the schema and optimized database access patterns so the platform could grow without the database becoming the bottleneck.

Partial modernization to Lambda microservices. We identified the parts of the application that would benefit most from serverless and migrated them to AWS Lambda. The variable load from marketplace crawls matched what Lambda does best — scale to demand, cost nothing when idle. We kept the rest of the application intact rather than rewriting what didn’t need rewriting.

A real CI/CD pipeline. Deployments had been a ceremony. We built a fully automated pipeline on AWS CodePipeline and CodeDeploy — build, test, deploy, with automated regression testing. Releases went from events to routine.

Scale that matches demand. The serverless architecture and optimized data model let IP Shark grow crawl volume and customer count without the platform becoming the limiting factor.

Cost discipline. Moving variable-load work to Lambda and rightsizing the remaining compute cut unnecessary spend. The team stopped paying for idle capacity.

Release velocity. With the automated pipeline and regression tests in place, IP Shark’s team could ship with confidence. What used to require careful coordination became the default path.

The modernization engagement closed. The platform moved into a managed operations retainer with Polymath — 24/7 monitoring, incident response, weekly AWS health scans, crawl anomaly management, monthly reports.

The engineers who built the platform are the ones who run it. Continuity is the fastest diagnostic tool.