Illusion AI

Illusion provides a visual, drag-and-drop interface for composing multi-step generative AI workflows without writing code. Users connect pre-built AI blocks (text generation, image generation, data processing) into directed acyclic graphs, with data flowing between nodes via implicit type coercion and JSON serialization. The platform abstracts away API authentication, prompt engineering, and model selection through templated blocks that expose only high-level parameters.

Illusion abstracts away differences between generative AI providers (OpenAI, Anthropic, etc.) by exposing a unified interface for text and image generation. Users select a model from a dropdown without managing API endpoints, authentication headers, or provider-specific parameter mappings. The platform translates high-level parameters (temperature, max tokens, system prompt) into provider-specific API calls, handling rate limiting, retries, and fallback logic transparently.

Illusion maintains a version history of workflow changes, allowing users to view previous versions, compare changes, and rollback to earlier versions if needed. Each version is timestamped and includes metadata about what changed (e.g., 'updated prompt', 'changed model'). Users can restore a previous version with a single click, and the platform prevents accidental overwrites by requiring confirmation before publishing breaking changes.

Illusion allows users to define error handling strategies for workflow steps, including automatic retries with exponential backoff, fallback workflows, and error notifications. Users can configure which errors trigger retries (e.g., rate limits, timeouts) versus which errors should fail the workflow (e.g., authentication errors). Failed workflows can trigger alternative workflows or send alerts to users.

Illusion exposes a visual editor for crafting and iterating on prompts and model parameters (temperature, max tokens, system instructions) without touching code. Users can test prompts in real-time against live models, see token counts and estimated costs, and save prompt variations as templates. The interface provides guidance on prompt best practices and suggests parameter adjustments based on output quality.

Illusion allows users to deploy built workflows as standalone applications with a shareable URL, enabling non-technical users to distribute AI tools to colleagues or customers. The freemium model provides free tier deployments with usage limits (e.g., requests per month), and paid tiers scale based on actual API consumption. The platform handles hosting, scaling, and billing — users only pay for the underlying AI API calls, not infrastructure.

Illusion provides a library of pre-built workflow templates (e.g., 'Email Writer', 'Image Background Remover', 'Customer Support Chatbot') that users can clone and customize. Templates include example prompts, parameter configurations, and integration patterns. A community marketplace allows users to publish and discover workflows created by other users, enabling rapid bootstrapping of new applications without starting from scratch.

Illusion supports conditional branching in workflows, allowing users to route execution based on model outputs or user inputs. Users can define if-then-else logic visually (e.g., 'if sentiment is negative, route to escalation workflow; otherwise, respond with generated message'). Conditions are evaluated at runtime against structured or unstructured data, and multiple branches can execute in parallel or sequence.

Illusion provides blocks for extracting structured data from unstructured model outputs (e.g., parsing JSON from text generation, extracting entities from summaries, converting images to text via OCR). Users can define extraction schemas visually or via templates, and the platform uses regex, JSON parsing, or secondary AI calls to extract and validate data. Extracted data can be passed to downstream workflow steps or exported to external systems.

Illusion allows workflows to call external APIs (REST, webhooks) to fetch data, trigger actions, or send results to third-party systems. Users configure API endpoints, authentication (API keys, OAuth), request/response mapping, and error handling visually. The platform handles HTTP requests, retries, and response parsing, allowing workflows to integrate with CRMs, databases, messaging platforms, and custom backends without code.

Illusion supports batch processing of multiple inputs (e.g., processing 1000 images or generating summaries for 100 documents) by queuing requests and executing them asynchronously. Users upload batch data (CSV, JSON, file list) and the platform distributes execution across available resources, providing progress tracking and result aggregation. Batch jobs can be scheduled to run at specific times or triggered by webhooks.

Illusion provides a dashboard showing API usage (requests, tokens, model calls), estimated costs, and performance metrics (latency, error rates). Users can set usage alerts and spending limits to prevent unexpected bills. Analytics break down usage by workflow, model, and time period, helping users identify optimization opportunities. The platform provides recommendations for cost reduction (e.g., switching to cheaper models, batching requests).