Customer-facing analytics,
built in code. Trusted at scale

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.

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.

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.

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.

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.

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.

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.

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.

Yes. Embeddable connects to your data source and does not need to copy your underlying customer data into a separate analytics warehouse. Data models define what can be queried and by whom, while secure connection and access patterns keep control with your team.

Embeddable supports security patterns for production customer-facing analytics, including encrypted connections, secure access controls, row- and/or database-level security, and tenant-aware permissions. Embeddable is SOC 2 Type II certified and GDPR compliant.

Embeddable is built for single-tenant and multi-tenant applications. You can enforce row-level security, scope access by user or tenant, and support different database environments or data structures depending on your architecture.

Embeddable’s AI direction is built around guardrails. AI-generated queries and insights should operate through the customer-defined semantic layer, with approved metrics, row-level access, validation, and auditability applied before anything is rendered to users.

The goal is for AI-assisted dashboard creation to use your actual component library and design system, rather than generic chart defaults. That means AI can help accelerate creation while the output still feels like part of your product.

Start by speaking with the Embeddable team or exploring the developer documentation. Most teams begin by connecting a data source, defining models and components, and embedding a first dashboard or chart into their application.