Systems | Development | Analytics | API | Testing

Introducing the Katalon MSP Program: Deliver Scalable QA Services Without Building Custom Frameworks

Katalon is introducing a new MSP Program designed for our official solution and service partners. Built for partners delivering QA services across multiple customer engagements, the True Platform MSP Program offers a more flexible way to scale delivery with Katalon’s all-in-one testing platform.

Custom MCP Server vs. AI Data Gateway: Which Is Right for Enterprise AI?

The Model Context Protocol (MCP) is quickly becoming the standard for how large language models connect to enterprise data. As adoption accelerates, engineering teams face a foundational decision: build a custom MCP server from scratch, or adopt an AI data gateway that ships with MCP support, security, and governance out of the box. Both paths have real tradeoffs. This post breaks them down so you can make the right call for your stack, your team, and your risk profile.

Why Audit Logs Matter for AI Governance | DreamFactory

Audit logs are essential for making AI systems accountable, reliable, and compliant with regulations. They act as a record-keeping system, documenting every critical interaction within an AI system, such as user prompts, model decisions, and policy enforcement. Here's why they are crucial: Audit logs are not just a legal requirement - they are a key part of managing AI systems effectively and minimizing risks.

Top 12 Platforms for Validating and Handling Errors in CSV Files

The best platforms for validating and handling errors in CSV files combine schema enforcement, real-time error detection, and automated remediation within a unified pipeline. Integrate.io ranks as the top choice for data teams that need enterprise ETL solutions for seamless CSV handling and error detection, offering a no-code interface, robust pre-load validation, and deep connector coverage.

How Manufacturing Leaders Deploy AI Faster with Governance-First Architecture

AI workflows for manufacturing need to be deployed quickly. Quality control systems, predictive maintenance tools, and supply chain optimization algorithms may be going live, yet compliance infrastructure is lagging behind. The result is a familiar pattern: pilots that prove out technically but stall before production because they can’t clear audit, safety, or regulatory review.

Insurance Mobile App Development: Compliance, Cost and Future Trands

Insurtech is picking up pace fast. And it’s not only because of new tech coming in, but also because people today simply expect things to be easier, quicker, and more transparent. That said, technology is still doing most of the heavy lifting here. We’re already used to apps simplifying our everyday lives, so when insurance starts doing the same, it naturally feels like the right move.‍ So, what is Insurtech? And why is it expected to reach $152.4 billion by 2030?

AI Testing Best Practices - Why Human Governance Separates Real AI Platforms from Hype

There is a scenario playing out in QA teams everywhere right now. A team adopts an AI testing tool, runs it for the first time, and gets 300 test cases in minutes. The demo worked. The ROI math looked great. But three sprints later, 60 of those test cases are validating requirements that were updated in the last sprint. Twenty more test a user flow that was deprecated. The AI performed exactly as advertised. The governance system never existed.