Point Luna

Point Luna provides a relational warehouse of major public datasets that are pre‑cleaned, documented, and joined at the geographic level, making them query‑ready within days.

Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Analysts and researchers often spend weeks cleaning, documenting, and joining disparate public datasets before they can ask substantive questions, delaying data‑driven insights and decision‑making.

Solution

Point Luna offers a relational warehouse of major public datasets that are pre‑cleaned, documented, and joined at the geographic level, making them query‑ready within days. An AI‑powered MCP (Model‑Centric Platform) server connects the warehouse to existing AI clients such as Claude, ChatGPT, and Cursor, allowing users to ask plain‑English questions and receive answers directly grounded in the source data. The platform leverages cloud‑based BigQuery for scalable querying and provides a simple access workflow through a Google Cloud project. By automating data preparation and enabling natural‑language querying, Point Luna accelerates analysis for analysts, think‑tanks, nonprofits, and corporate teams.

Target Audience

Primary users include academic researchers, policy think‑tanks, nonprofit impact analysts, and corporate analysts who need rapid access to reliable public data for evidence‑based work.

Features

Comprehensive relational database of cleaned, documented public datasets joined by geographic identifiers
AI‑driven MCP server that integrates with major LLM clients for plain‑English, data‑grounded queries
Cloud‑native BigQuery integration for fast, scalable SQL querying without local infrastructure
Quick onboarding via Google Cloud project with pre‑starred Point Luna dataset and sample queries
Ongoing research publications that release data, methodology, and analysis openly