Operations | Monitoring | ITSM | DevOps | Cloud

Traditionally, companies had to use separate tools for ETL, data storage, and analytics. Often, this resulted in slow, complex, and expensive data workflows A good example is PwC’s Deals, Insights & Analytics (DIA) team, which faced similar challenges. According to Microsoft, bespoke solutions often took months to build and were difficult to merge, slowing projects and driving up costs. That changed when PwC adopted Azure Synapse Analytics. The result?

If you’re like most developers, you know that writing code isn’t the bottleneck anymore. AI has made it faster than ever, and chances are you’re already using it. Yet, delivering software is still complex because of everything else you have to manage: fixing vulnerabilities, reducing tech debt, cleaning up feature flags, ensuring test coverage, writing documentation, and the list goes on. That’s why we built Rovo Dev, a context-aware AI agent for developers.

This post is a deep dive into how we improved the P95 latency of an API endpoint from 5s to 0.3s using a niche little computer science trick called a bloom filter. We’ll cover why the endpoint was slow, the options we considered to make it fast and how we decided between them, and how it all works under the hood.

Are your deployments getting stuck waiting for approvals? Your code is ready. Your tests are green. But your ServiceNow change ticket is still holding up the release. In most organizations, this isn’t a people problem or a process problem. It’s an evidence problem. Every release has to prove that it met the required checks — tests, scans, reviews, and approvals. But when that proof isn’t instantly available, everything slows down.