Baseplate

Baseplate abstracts database and API connection complexity through a declarative configuration layer that automatically infers schemas from connected sources. Rather than requiring custom code for each integration, users define data sources through a UI or configuration file, and the system handles authentication, credential management, and schema discovery. This approach eliminates boilerplate integration code and enables non-technical users to connect PostgreSQL, MySQL, REST APIs, and other sources without writing backend logic.

Baseplate maintains live synchronization between connected data sources and AI models through a polling or webhook-based architecture that detects changes and updates the AI system's context window. Rather than requiring manual data refresh or static snapshots, the system continuously monitors source data and ensures the LLM always operates on current information. This enables AI assistants to answer questions about up-to-date inventory, customer records, or transaction history without staleness.

Baseplate provides a unified query interface that abstracts differences between heterogeneous data sources (SQL databases, REST APIs, document stores) and routes queries to the appropriate backend. When an AI model needs data, it calls a single Baseplate endpoint that translates the request into source-specific query syntax (SQL, GraphQL, REST parameters) and aggregates results. This eliminates the need for AI systems to understand multiple query languages or handle source-specific error handling.

Baseplate centralizes credential management and authentication handling across all connected data sources, supporting multiple auth patterns (API keys, OAuth 2.0, database connection strings, service accounts) through a unified vault. Rather than embedding credentials in AI prompts or application code, the system securely stores and rotates credentials, and AI systems reference data sources by logical name. This eliminates credential exposure risks and simplifies credential rotation without redeploying AI models.

Baseplate exposes connected data sources as callable functions that AI models can invoke through function-calling APIs (OpenAI, Anthropic, etc.), automatically generating function schemas from inferred data source schemas. When an AI model decides it needs data, it calls a Baseplate-generated function with appropriate parameters, and the system executes the query and returns results. This enables AI agents to autonomously fetch data without explicit prompting or manual orchestration.

Baseplate enforces row-level and column-level access control policies, allowing administrators to define which AI agents or users can access specific data subsets. The system evaluates permissions at query time, filtering results based on policies defined in the Baseplate console or configuration. This enables multi-tenant AI systems where different customers or teams see only their own data, without requiring separate databases or custom query logic.

Baseplate provides a low-code interface for defining data transformations (filtering, aggregation, field mapping, computed columns) that execute before data reaches the AI model. Users define transformations through a visual builder or configuration language without writing code, and the system applies them during query execution. This enables data normalization and enrichment without requiring separate ETL pipelines or custom backend logic.

Baseplate caches query results and implements intelligent caching strategies (time-based TTL, change-based invalidation) to reduce redundant database queries and API calls. When an AI model requests data, the system checks the cache before querying the source, returning cached results if they're still valid. This reduces latency, decreases load on source systems, and lowers API costs for rate-limited sources.

Baseplate provides monitoring dashboards and logging for data queries executed by AI systems, tracking query latency, error rates, data freshness, and cost metrics. Teams can observe which data sources are accessed most frequently, identify slow queries, and debug data-related issues in AI responses. This enables data-driven optimization of AI systems and troubleshooting of accuracy or performance problems.

Baseplate exposes connected data sources through REST and GraphQL APIs, allowing AI systems and applications to query data through standard HTTP interfaces. Rather than requiring direct database connections or custom API development, teams use Baseplate-generated endpoints that handle authentication, query validation, and response formatting. This enables AI systems to fetch data through standard web protocols without database drivers or custom backend code.