Operations | Monitoring | ITSM | DevOps | Cloud

Israel’s healthcare system is widely recognized as one of the most digitally advanced in the world. Electronic health records are deeply embedded across hospitals, and platforms like Chameleon sit at the center of clinical operations. Patient data is captured, structured and accessible at nearly every stage of care delivery. But digital maturity alone does not guarantee operational efficiency.

Platform engineering has emerged as a response to the growing complexity of modern software delivery. As organizations adopt Kubernetes, microservices, CI/CD pipelines, and infrastructure as code, they are creating dedicated teams responsible for building and operating the internal platforms that power developer workflows.

Healthcare is shifting fast, and artificial intelligence is no longer a future concept sitting in research labs or pilot programs. It’s already embedded in clinical workflows, operational systems, and patient interactions, often in ways that feel subtle, uneven, and sometimes uncomfortable.

You’ve done it. Your machine learning model is live in production. It’s serving predictions, powering features, and quietly doing its job. Dashboards are green. There are no errors in the logs. Nothing appears broken. And yet, something is wrong. Predictions are getting less reliable. Users are waiting a little longer for responses. Conversion rates are slipping. Trust is eroding, but no alert fires, no system crashes, and no one knows there’s a problem until the damage has been done.

Over the past couple of weeks, as snowstorms and extreme cold swept across much of the Northeast, something interesting started happening on our end at OnPage. Our phones lit up. Not from healthcare teams or IT operations/tech teams, which is where many people expect us to be used, but from HVAC companies, contractors, and property management firms scrambling to prepare for what they knew was coming.

As we move further into 2026, we wanted to pause for a moment and reflect on what the past year looked like for OnPage, not just in terms of features shipped, but in how the platform evolved to better support the way teams actually work in high-stakes environments. 2025 was a foundational year for us.

Freshservice has become a trusted system of record for IT teams managing incidents, service requests, and operational issues at scale. Tickets are logged, categorized, prioritized, and tracked with discipline. SLAs are defined. Dashboards provide visibility. On paper, everything looks covered. Yet many teams still experience missed or delayed responses when incidents truly matter, especially after hours. The gap isn’t in ticket creation. It’s in what happens next.

Disclosure: This comparison is written by our product marketing team that works closely with IT operations and on-call workflows. While we build incident alerting software ourselves, this guide is designed to help teams understand how different tools fit different operational needs. We believe there is no single “best” tool. Only the right fit for a given team.

In Part 1, we talked about all the hidden complexity inside AI systems: the pipelines, GPUs, embeddings, vector databases, orchestration layers, and everything else that quietly determines how reliable an AI-first product really is. But all of that software still rests on something far less glamorous: the physical infrastructure underneath it.

As organizations continue to adopt OnPage across regions and operational environments, providing an experience that feels natural and intuitive for every user has become increasingly important. Clear communication is essential in time-sensitive workflows, and being able to use the app in one’s preferred language supports clarity, confidence, and consistency. To support our growing global user base, OnPage is introducing multi-language localization across its mobile applications.