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Embedded analytics has become a vital component of modern applications, providing users with real-time insights that drive smarter decisions. However, integrating these analytics seamlessly into your existing systems is often easier said than done.

Conversational analytics gives users a faster way to get insights by letting them ask direct questions instead of building reports. It reduces friction across the product and helps teams deliver clear answers without extra clicks or technical steps. The challenge appears when conversational analytics software relies on external AI services, which creates security and data-control risks. Reveal solves this with an architecture that keeps AI inside your environment and applies your existing rules to every request. You get a secure, flexible layer that supports natural-language queries without exposing your data.

In this article, we’ll explore the implications of limited customization in embedded analytics and why it’s a critical issue for CTOs, product managers, and development teams.

We are thrilled to announce the release of Reveal 1.6.7. This release introduces a series of new powerful analytics features and enhancements designed to offer superior customization, deeper integration, and more robust analytics capabilities.

Learn about the latest features and enhancements in Reveal – the powerful and easy-to-use embedded analytics platform.

The latest Reveal release, 1.6.1, is here, packed with improvements based on your feedback.

Introducing custom visualizations, interactive chart filtering and a host of other enhancements and bug fixes that help us provide you with the best embedded analytics solution you deserve.

Today we are excited to deliver 15 new features & product enhancements to the Reveal visualization editor, API, data sources, and docs.

Get an idea of what our plans look like, feature-wise, for 2023. Everything we add is based on customer feedback and demand. While we may add items that we believe make us competitive in the market or features that are based on the general market direction or analyst feedback, we are constantly adjusting and adapting to what you need to deliver successful software.

White label analytics allows SaaS products to embed dashboards and insights under their own brand, fully integrated into the application. Users interact with data inside the same interface, which improves adoption and keeps workflows uninterrupted. The key difference from basic embedding lies in integration depth. iFrame-based approaches limit customization and create disconnected experiences, while SDK and API-based platforms enable deeper control, better performance, and scalability across tenants. For SaaS teams, this affects retention, monetization, and development speed, turning analytics into a core product capability rather than an external add-on.