Livedocs

What is Livedocs?

Livedocs is a modern collaborative workspace designed for data teams who need to analyze, visualize, and share insights efficiently. The platform merges the power of traditional notebooks with the simplicity of application builders, allowing users to write SQL and Python code, query data from any source, and transform static reports into interactive applications. Built on high-performance engines like DuckDB and Polars, Livedocs eliminates setup complexity while providing a hybrid execution model that intelligently routes queries to the most efficient backend—whether that's in-memory processing, local file querying, or warehouse execution. The built-in AI agent can write queries, generate visualizations, clean data, and reason about results, acting as a collaborative data scientist. Teams benefit from real-time multiplayer editing, scheduled automation, and the ability to deploy analyses as live data apps that non-technical users can explore without touching code.

Key Features

• Unified Data Environment

  Work with Python, SQL, and AI models in a single reactive workspace. Connect to all major data warehouses, query local files via DuckDB, and blend data sources seamlessly without context switching.

• AI Data Assistant

  Built-in AI agent writes and optimizes queries, generates charts from natural language requests, cleans data automatically, and interprets analysis results in business language without requiring manual coding.

• Interactive Data Apps

  Transform static notebooks into dynamic applications with dropdowns, sliders, and input fields that trigger real-time updates. Deploy as live apps for stakeholders who need answers, not code.

• Performance-First Architecture

  Powered by Polars and DuckDB with intelligent query routing. Automatic dependency tracking ensures only changed cells rerun, caching optimizes performance, and hybrid execution selects the fastest compute path.

• Real-Time Collaboration

  Multiple team members edit notebooks simultaneously with live presence, comments, and shared workspaces. Publish documents as live apps, static views, or collaborative notebooks tailored to different audiences.

• Scheduled Automation

  Automate data pipelines and reporting workflows with scheduled notebook runs. Built-in secrets management and key-value stores enable stateful automation without external tools.

Use Cases

• Data Exploration & Analysis : Analysts and data scientists quickly explore datasets, test hypotheses, and extract insights using Python, SQL, and AI without waiting for notebook startup or wrestling with complex configurations.
• Business Intelligence & Reporting : Finance teams and business analysts create interactive dashboards and reports that refresh automatically. Stakeholders drill into data and ask follow-up questions without requesting new reports.
• Investment & Market Research : Investment professionals combine earnings data, market trends, and financial forecasts into coherent narratives with interactive visualizations that update as new data arrives.
• Data Storytelling : Teams craft compelling data narratives with Python-generated visuals, AI-written interpretations, and interactive elements that make findings impossible to ignore across the organization.
• Automation & Workflows : Deploy scheduled data pipelines that clean data, run analysis, and refresh insights on a cadence. Eliminate manual weekly report generation and keep metrics current without DevOps involvement.

FAQs