Author

Most enterprise BI platforms were built for internal reporting, not for modern SaaS products utilizing customer-facing analytics. They sit outside your application, fragment your UX, and delay decision-making. Modern enterprise software requires analytics that are intuitive, embedded, and self-service by design. Reveal was built to deliver exactly that—with no iFrames, no handoffs, and no compromises.

Self-service BI empowers users to explore and act on data without relying on technical teams or submitting support tickets. It gives business users the tools they need to generate insights, build reports, and make decisions in real time, on their own.

For SaaS products and internal platforms, embedding self-service BI directly into your application extends that value by delivering a seamless and native analytics experience that is fast, scalable, and fully customizable.

When starting a new project, deciding which Angular dashboard library to use is crucial for the project’s success. A well-chosen library can streamline your development process and enhance your application’s analytics capabilities.

The fastest way to monetize your data is by embedding white-labeled dashboards directly into your product. While the opportunity is clear, the execution isn’t always easy. Building in-house drains time, money, and dev resources, and choosing the wrong partner can lead to the same bottlenecks that stall monetization efforts.

The best way to deliver insights in your SaaS product is to embed them directly where users work. No switching tools. No report delays. Building analytics in-house burns time and dev resources. Reveal is purpose-built embedded analytics for SaaS platforms, with a true SDK, full white label control, and fixed pricing that scales. You get in-app self-service analytics without slowing down your roadmap.

Choosing the wrong embedded analytics platform slows your roadmap, increases dev burden, and weakens the product experience. Many tools check feature boxes but fail on pricing models, embedding control, integration fit, and long-term scale.