Drafter AI

Provides a drag-and-drop canvas interface for constructing multi-step AI workflows without writing code. Users connect pre-built nodes (LLM calls, data transformations, API integrations) via visual edges to define execution flow, with the platform compiling these visual definitions into executable task graphs that handle sequencing, error handling, and state passing between steps.

Offers a curated set of reusable workflow nodes that abstract away provider-specific API details for common AI operations (text generation, summarization, classification, embeddings). Each node wraps LLM provider APIs (OpenAI, Anthropic, Cohere, etc.) behind a unified interface, allowing users to swap providers or adjust model parameters without rebuilding workflows. Nodes likely include parameter templates, input/output schema definitions, and error handling logic.

Provides built-in error handling and retry mechanisms for workflow steps without requiring code. Users can configure retry policies (exponential backoff, max attempts, delay between retries) and error handlers (fallback values, alternative steps, notifications) through the UI. The platform automatically catches API failures, timeouts, and LLM errors, routing them to configured error handlers rather than failing the entire workflow.

Provides authentication and authorization mechanisms for protecting deployed workflow APIs and web interfaces. Users can configure API key authentication, OAuth integration, or basic auth through the UI. The platform supports role-based access control (RBAC) to restrict who can view, edit, or execute workflows. Authentication is enforced at the API endpoint level without requiring code.

Automatically deploys built workflows as hosted web applications or APIs without requiring infrastructure management. The platform handles containerization, scaling, and API endpoint generation, exposing workflows via HTTP endpoints that can be called from external applications. Users can configure authentication, rate limiting, and monitoring through the UI without touching deployment configuration files or cloud provider consoles.

Provides an interactive UI for crafting and refining LLM prompts with real-time preview and parameter adjustment. Users can modify system prompts, adjust temperature/top-p/max-tokens sliders, and test prompts against sample inputs without leaving the workflow builder. The interface likely includes prompt templates, variable injection syntax, and execution history to track how prompt changes affect outputs.

Provides pre-built nodes for common data manipulation tasks (JSON parsing, text splitting, field extraction, filtering, aggregation) that operate on workflow data without requiring code. These nodes use declarative configuration (e.g., JSON path selectors, regex patterns, field mappings) to transform data between workflow steps. The platform likely includes a visual data mapper for complex transformations and supports chaining multiple transformation nodes.

Enables workflows to call external APIs and receive webhook events through pre-built HTTP request nodes. Users configure API endpoints, authentication (API keys, OAuth, basic auth), request headers, and body payloads through the UI without writing HTTP code. The platform handles request/response parsing, error handling, and retry logic. Webhook support allows external systems to trigger workflows via HTTP POST events.

Maintains a version history of workflow definitions and execution logs, allowing users to view past workflow runs, inspect inputs/outputs, and rollback to previous versions. The platform tracks changes to workflow structure and configuration, with the ability to compare versions and restore earlier states. Execution history includes timestamps, status (success/failure), and detailed logs for debugging.

Offers a freemium tier with limited monthly API calls and a pay-as-you-go pricing model for higher usage. The platform provides real-time cost tracking and usage dashboards showing API call counts, LLM token consumption, and estimated monthly bills. Users can set spending limits and receive alerts when approaching thresholds. Pricing is transparent with per-API-call and per-token costs clearly displayed.

Provides pre-built workflow templates for common AI applications (customer support chatbots, content generation, data classification, lead scoring, etc.). Templates include pre-configured nodes, example prompts, and sample data, allowing users to fork and customize templates rather than building from scratch. Templates are likely community-contributed or curated by Drafter, with ratings and usage metrics to guide selection.

Enables workflows to make decisions based on data conditions using if/then/else branching nodes. Users define conditions (e.g., 'if LLM confidence > 0.8, route to approval; else route to manual review') through a visual condition builder without writing code. The platform supports multiple condition types (string matching, numeric comparisons, regex patterns, JSON path evaluation) and allows chaining conditions for complex decision trees.