Every TaruviBase application ships with the same production-grade backend infrastructure on day one, provisioned, secured, observable, and ready to host real enterprise SaaS. AI agents handle the front end and use the TaruviBase MCP layer to build and interact with the backend.
A short guided walkthrough of the TaruviBase platform: datatables and schemas, auth and permissions, serverless functions, the native MCP layer, file storage, auto-generated APIs, analytics, and secrets and events, in one managed, multi-tenant backend from EOX Vantage.
Schemas with relationships, hierarchy traversal, and full-text search. Twenty-plus filter operators and version-controlled migrations let your data model evolve safely as the product grows.

Four authentication strategies supported out of the box. Policy-driven authorization defined once and enforced automatically across the entire backend, no authorization logic scattered through your code.

Serverless without a server. Run code on demand, on schedule, or in response to a database event, and proxy webhooks to the third-party tools your team already uses, including Zapier, n8n, and Make.

Cursor, Claude, Copilot, Windsurf, and your own agents connect directly to the backend, query data, understand schema, and take action, with policies enforced at the platform layer, not in code the AI has to remember to write. Safe at speed.

Public and private access policies, MIME type controls, and authentication-layer integration for secure uploads and downloads. Audit-logged like everything else.

The auto-generated API reflects your schemas, your policies, and your relationships. JavaScript/TypeScript and Python SDKs with full type information ship out of the box.

Build reporting and dashboards without exposing direct database access. Templates are versioned, reviewed, and parameter-validated before they run.

Encrypted configuration at app and site levels, JSON-schema validated. Subscribe serverless functions to system events: react to data changes, user actions, or scheduled triggers without polling.

Walk through your data model with one of our engineers. We'll show you exactly how schemas, policies, and the MCP layer fit together, on your use case.
TaruviBase schemas support relationships, foreign keys, joins, hierarchy traversal, and full-text search. You define the data model with a visual schema builder and declarative migrations, then filter it with more than 20 operators, all with per-tenant isolation enforced at the platform layer.
Four authentication strategies work out of the box: JWT, OAuth, SAML, and API keys. Authorization is centralized and declarative, defined once and enforced automatically across the entire backend, with an automatic, queryable audit trail on every check.
TaruviBase uses RBAC for straightforward, role-based permissions and ABAC for context-driven scenarios where access depends on attributes. Both are defined in centralized policies rather than scattered through your code, and ABAC applies to file storage on every read and write.
You run Python functions on demand with HTTPS endpoints, on a cron-style schedule, or in response to database events. Functions can also proxy webhooks to third-party tools including Zapier, n8n, and Make.
Every TaruviBase backend ships with a native MCP endpoint per app with zero config. Cursor, Claude, Copilot, Windsurf, and your own agents connect directly to query data, understand the schema, and take action, with policies enforced at the platform layer and all AI access recorded in audit logs.
Yes. The auto-generated REST API reflects your schemas, policies, and relationships, with endpoints for every schema plus filters and joins. A JavaScript/TypeScript SDK with full type generation and a Python SDK with async and sync clients ship out of the box, and an OpenAPI spec is exposed at /spec.