Question 1

How is Embeddable different from building dashboards with AI yourself?

Accepted Answer

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.

Question 2

What does “dashboards as code” mean in Embeddable?

Accepted Answer

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.

Question 3

Can Embeddable work with AI-assisted development workflows?

Accepted Answer

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.

Question 4

What does Embeddable handle underneath the experience?

Accepted Answer

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.

Question 5

Will Embeddable dashboards still feel native in my product?

Accepted Answer

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.

Question 6

Does Embeddable use iframes?

Accepted Answer

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.

Question 7

Can customers build or customise their own dashboards?

Accepted Answer

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.

Question 8

How does Embeddable keep self-serve analytics governed?

Accepted Answer

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.

Question 9

Does customer data stay in our database?

Accepted Answer

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.

Question 10

How does Embeddable secure customer-facing analytics?

Accepted Answer

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

Question 11

How does Embeddable support multi-tenant applications?

Accepted Answer

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.

Question 12

Can AI or agents query data safely through Embeddable?

Accepted Answer

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.

Question 13

Will AI-generated dashboards use our own product components?

Accepted Answer

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.

Question 14

How do we get started with Embeddable?

Accepted Answer

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.

Dashboards as Code

Define dashboards directly in your codebase. Version-controlled, agent-ready, deployed through your existing release process.

In your codebase

Native in your product

A single source of truth in your repo

Three steps. No new tooling.

Full-fidelity dashboards, expressed as YAML

Built for agentic development workflows