Systems | Development | Analytics | API | Testing

Ep 93 | When Everyone Has AI, Your Data Sets You Apart

After 23 years in machine learning and data science, Cao Hong has seen plenty of AI projects come and go. His measure of success is simple: did it create value for the business? Recorded at *EVOLVE26* Singapore, this episode of The AI Forecast brings Paul Muller together with Cao Hong, Principal Director of Data Apps at NCS, to explore how organizations can turn AI experimentation into measurable business impact.

How to Build a Self-Service AI Platform Without Losing Control of GPUs, Data, or Security

Most enterprise AI infrastructure conversations land on the same tension. Builders want self-service: instant access to GPUs, a stable endpoint for a model, a notebook, or a fine-tuning run that starts without a ticket. Platform teams want per-team quotas, identity-aware access, audit trails, tenant isolation, and cost attribution they can defend. Both sides are right, and most platforms make you choose between them because self-service and control get layered onto a base that was designed for neither.

Tricentis Webinar Series: What customers are saying about Agentic Performance Testing

As Tricentis customers continue to adopt Agentic Performance Testing, Product Marketing Manager Jason Secola sat down with APT's product manager, Twan Koot, to discuss how customers are making use of its agentic capabilities. Here, he shares customer feedback. Key highlights.

From BI to Agentic AI: Why the Dashboards You Built Are the Foundation for Agents

Two questions stall almost every agentic AI rollout: Is it secure? And what is this going to cost us? Both get harder to answer the longer you wait to ask them, and easier to answer than most teams assume. If you've already invested in a robust data analytics platform like Qlik, you're closer to a secure, governed, and cost-effective agentic AI deployment than the market's “rip and replace” narrative suggests.

Your Guide to Perforce Autonomous Testing

Software testing is struggling to keep pace with modern release cycles. More code, faster deployments, fragmented tools, and increasing quality demands are creating bottlenecks for QA, engineering, and DevOps teams. Discover how Perforce Autonomous Testing transforms the way teams validate software by bringing functional, performance, web, mobile, and desktop testing together through a unified AI-driven experience. Using natural language, teams can define testing intent, automate execution, orchestrate complex workflows, and gain actionable insights faster than ever before.

How to Build an Effective Database Testing Strategy for Reliable Releases

Green build pipelines lie. Your automated unit suites can execute without a single failure, API contracts can validate cleanly, and browser regression sweeps can pass with pristine checkmarks. Yet the moment a release hits production, software must interact with state. A single unindexed foreign key, an overlooked lock escalation on a high-throughput table, or an uncalibrated default constraint will bring an entire platform to a halt while your CI dashboards remain reassuringly green.

SSO for Healthcare: How Single Sign-On Works in Clinical Environments

Healthcare organizations are abandoning legacy identity systems. In 2026, a 250-bed hospital still running standalone credentials across EHR, pharmacy, and billing systems faces escalating regulatory risk, operational friction, and insider threat exposure. Enterprise SSO for healthcare solves this by unifying identity across HIPAA-regulated applications while enforcing zero-trust principles at every access point. But SSO for healthcare isn't just Okta or Azure AD out-of-the-box.

Honoring Absa: Achieving 96% less test creation time with SmartBear QMetry and SmartBear Reflect

At SmartBear, we’re dedicated to empowering teams by delivering application integrity: continuous, measurable assurance that their software works as intended, even as AI accelerates the SDLC. The AI Test Innovators Award: QMetry + Reflect celebrates customers who are using AI to reimagine how they create, maintain, and manage tests – without sacrificing rigor or reliability.