Key Takeaways from Our AI Quality Discussion
AI systems change faster than traditional QA models can react, which means quality risks now emerge in real time rather than at release.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Build Modern AI-Ready Infrastructure Without Starting Over
Modernization has reached a tipping point. For organizations running mission-critical workloads—including increasingly demanding AI-driven applications—the traditional approach often adds risk instead of reducing it. But does modernization have to mean costly investments, downtime, and uncertainty? Not anymore.
Building Bitrise's context-aware, browser-integrated AI copilot that keeps engineers in control
Note: This article is the second in this series. Check out the first one here: Why we ditched frontier AI agents and built our own.
The AI Hacks Coming in 2026 (And Why No One's Ready) #shorts
Curious — Do you think companies are ready for the governance piece? Or are we about to learn the hard way?
Oracle's AI momentum and why quality assurance matters more than ever
After attending Oracle AI World, one thing is clear: Oracle is all in on AI. From embedded agents across Fusion Applications to the AI-native Oracle Database and OCI’s massive model-training capabilities, Oracle is positioning itself as the platform where enterprise AI comes to life. The energy was infectious. Every session, demo, and conversation carried a sense of acceleration: faster decisions, faster workflows, faster innovation.
Cloudera and Protegrity: Securing Your Open Data Lifecycle
Protegrity’s Jessica Hammond joins Cloudera to discuss their data-centric approach. Learn how the partnership secures unstructured data and LLMs, enabling frictionless, automated AI and analytics on the Cloudera platform. Learn how Cloudera and Protegrity secure your AI data.
AI-Powered Integration: Turning Complex Workflows into Simple Commands
Data integration has long been one of the most time-intensive parts of enterprise IT. Connecting multiple systems, reconciling formats, and ensuring data reaches its destination reliably often requires weeks of preparation before the first record moves. But with AI-powered integration, that timeline compresses dramatically. What once took weeks can now be designed, validated, and delivered in minutes.
How to Automate Data Pipelines with AI-ETL Tools
Organizations waste an average of $12.9 million annually due to poor data quality. Traditional ETL processes built on hard-coded rules break with schema changes, batch processing misses real-time insights, and manual mapping creates bottlenecks that delay critical business decisions by days or weeks.
How to Choose AI-Powered ETL for Non-Technical Teams
Data teams spend 45% of their time on data preparation, which stifles business growth and delays critical insights. With the ETL market projected to grow from $533 million to $1.28 billion by 2034, businesses face an overwhelming array of choices. Yet traditional ETL tools require specialized coding expertise that non-technical teams simply don't have, creating dangerous dependencies on overburdened IT departments.
Enterprise AI that's Ready for Serious Work: Introducing Appian Composer and Agent Studio
AI agents have exploded. And we use this term intentionally. Tech vendors rushed to put out agent products that don’t stand up to enterprise use with results that were mostly underwhelming and sometimes even catastrophic. Appian took a different approach. While companies are now scrambling to validate the efficacy of their AI, Appian’s AI is field-tested, so the products you get are ready to use and safe to deploy from day one.