Metaforms

Transforms user intent expressed in natural conversation into structured survey/form definitions through multi-turn dialogue. The system uses LLM-based intent extraction to parse user goals, infer question types, and generate question hierarchies with conditional logic, then renders these as interactive forms without requiring manual form builder interaction. This approach reduces form creation from hours of UI manipulation to minutes of conversation.

Automatically generates conditional question flows where subsequent questions adapt based on previous responses, inferred from user intent during form generation. The system maps response patterns to question dependencies using LLM-based logic inference, creating skip rules and dynamic question sets without manual rule configuration. This enables survey logic that would normally require manual conditional branching setup in traditional form builders.

Renders forms as conversational chatbot interfaces where questions appear sequentially in a chat-like format rather than as traditional static form fields. This interaction pattern uses message-based UI rendering with natural language question phrasing, creating a more engaging experience that increases response completion rates. The system collects responses through conversational input (text, buttons, selections) rather than form field submission.

Collects form responses in real-time and renders them in a dashboard with basic aggregation metrics (response counts, completion rates, average ratings). The system provides immediate visibility into response patterns through charts and summary statistics without requiring manual data export or analysis. Analytics update as new responses arrive, enabling live monitoring of survey campaigns.

Generates shareable form URLs that can be distributed via email, messaging, or embedded on websites for response collection. The system manages form access control, response tracking, and respondent identification through URL parameters and optional authentication. Forms can be shared publicly or restricted to specific audiences through link-based access controls.

Suggests improvements to form questions based on best practices and research methodology, using LLM analysis to identify ambiguous phrasing, leading questions, or missing follow-ups. The system can rewrite questions for clarity, suggest additional questions to fill research gaps, and flag potential bias in question design. Refinements are presented as suggestions that users can accept or reject.

Exports collected responses in multiple formats (CSV, JSON) and integrates with external tools through API or webhook integrations. The system enables data pipeline connections to analytics platforms, CRM systems, or data warehouses for downstream analysis. Exports include raw response data, aggregated metrics, and optional respondent metadata.

Offers free tier with limited form creation and response collection, with automatic tier progression to paid plans as usage increases. The system tracks form count, response volume, and feature usage to determine tier eligibility, enabling users to start free and upgrade only when needed. Pricing is transparent with clear upgrade paths.