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Where is AI used in Toucan?

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Target Audience: Non technical users & Developers

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TL;DR

Toucan I integrates Large Language Models (LLMs) across the platform to automate metadata generation, query construction, and visualization.

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When to use this

Use this page to identify which parts of the analytics workflow support automation and where the AI assistant replaces manual configuration.

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AI Integration Points

Toucan AI utilizes artificial intelligence at multiple stages of the analytics lifecycle to process data schemas and generate content.

1. Automated Metadata Enrichment

  • Contextual Analysis: Toucan AI scans database schemas to generate table and column descriptions.

  • Semantic Inference: Toucan AI identifies field types (Category, Date, Metric) to prepare data for visualization.

2. Chart and Dashboard Generation

  • Natural Language Processing: The AI assistant converts text prompts into SQL queries and visualizations.

  • Visual Selection: The AI assistant identifies the chart type best suited for the requested metrics and dimensions.

  • Step Explanation: Toucan AI provides a technical summary of how the query was constructed and the logic applied to the data.

3. Conversational Exploration

  • Assistant Interface: The AI assistant is available in the Library and on dashboards for ad-hoc data questions.

  • Pattern Detection: The AI assistant identifies statistical trends within tables and suggests relevant dimensions for further exploration.

4. Embedded Conversational Analytics

  • End-User Access: The AI assistant can be integrated into host applications via web components.

  • Self-Service: End-users can generate custom charts and queries using the AI assistant within the embedded environment.

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Technical Architecture

Toucan AI utilizes a multi-layered approach to interact with data securely.

  • Execution Model: AI-generated visualizations are based on real-time query execution against your connected database.

  • Data Security: Toucan AI interacts with schema metadata and sampled values to build prompts; it does not move entire datasets into the LLM.

  • Determinism: All generated charts are derived from actual data values rather than synthetic or guessed samples.

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Constraints

  • Validation: All AI-generated metadata and charts should be reviewed for accuracy before production deployment.

  • Schema Dependence: The quality of the AI assistant output is directly proportional to the clarity of the defined metadata.

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