Preset

Overview

Preset provides an AI-native business intelligence platform built on Apache Superset. It offers managed cloud, on-premise, and embedded analytics solutions for data visualization, exploration, and conversational AI. The company was founded by the creator of Apache Superset.

Key Features

• Managed Apache Superset — Preset offers a fully managed cloud service for Apache Superset, providing access to the latest features without self-maintenance.
• AI Chatbot — The platform includes an AI Chatbot for natural language querying, enabling users to ask questions and generate charts conversationally.
• Preset Agent Skills — An open-source library provides installable AI guidance packages that equip AI agents with domain expertise for working with Preset and Apache Superset.
• Interactive Dashboards — Users can create interactive dashboards and visualizations using a drag-and-drop interface or SQL editor.
• Semantic Layer — A semantic layer allows for last-mile SQL data transformations and the definition of dimensions, metrics, and virtual datasets.
• Row-Level Security (RLS) — The platform supports row-level security to prevent unauthorized access to sensitive data within dashboards.
• Deployment Flexibility — Preset offers deployment options including cloud-hosted, managed cloud, on-premise, and embedded analytics.

Who It's For

Preset targets business users, analysts, data engineers, and platform teams seeking to leverage open-source business intelligence. It is suitable for organizations that require scalable data visualization, self-service analytics, and AI-powered insights across various departments. The platform supports both SQL-savvy analysts and business users with no-code data exploration capabilities.

Notable Strengths

Preset's primary strength lies in its foundation on Apache Superset, with the company contributing over 75% of all commits to the open-source project, ensuring deep expertise and continuous development. The introduction of Preset Agent Skills provides a unique approach to AI integration, offering auditable, domain-specific instruction sets for AI agents, which enhances accuracy and operational safety in analytics environments. Its agnostic approach to underlying data architecture allows organizations to leverage existing data infrastructure without additional ingestion layers.