Embeddable

How is Embeddable different from building dashboards with AI yourself?

AI can help you scaffold dashboards, models, and UI faster. But customer-facing analytics still needs to become a production system: permissions, trusted metrics, tenant isolation, performance, auditability, and ongoing maintenance. Embeddable gives your team the infrastructure layer underneath, so developers and agents can move faster without owning the full long-term system burden.

What does “dashboards as code” mean in Embeddable?

Dashboards as code means your analytics experience can be defined, versioned, and managed in a developer workflow. Your team keeps control of components, layouts, data models, theming, and product logic, while Embeddable handles the production layer underneath: performance, security, self-serve infrastructure, and auditability.

Can Embeddable work with AI-assisted development workflows?

Yes. Embeddable is being designed for teams using AI tools to accelerate dashboard development. The goal is to make it easier for developers and agents to scaffold, edit, and deploy customer-facing analytics while keeping the resulting experience production-ready, governed, and maintainable.

What does Embeddable handle underneath the experience?

Embeddable handles the parts that become hard to own at scale: secure access across tenants and roles, governed self-serve infrastructure, performance and caching, trusted definitions, auditability, and operational visibility. Your team owns the product experience; Embeddable handles the trusted layer beneath it.

Will Embeddable dashboards still feel native in my product?

Yes. Embeddable dashboards are designed to feel like part of your application, not a separate BI tool. You can use your own design system, components, and visual language, and dashboards render directly inside your application experience rather than through a generic embedded BI surface.

Does Embeddable use iframes?

No. Embeddable dashboards can be embedded using native web components, with support for modern front-end environments. This helps analytics feel more integrated, performant, and flexible than traditional iframe-based embedding.

Can customers build or customise their own dashboards?

Yes. Embeddable supports controlled self-serve, so your customers can explore, filter, save views, and create or extend dashboards within the boundaries you define. You control which datasets, metrics, components, and permissions are available, so users get flexibility without breaking trust or access rules.

How does Embeddable keep self-serve analytics governed?

Self-serve is governed through your data models, permissions, and access rules. Users can explore and create within the experience you expose to them, while metric definitions, role-aware access, and tenant boundaries remain enforced.