Persistent documents
Ideas, tasks, events, places, notes, and other app data remain available after refresh and across devices.
MongoDB Atlas provides the database backend. The builder selects and connects the right LLM for the experience. Both are ready when the app is deployed.
Ideas, tasks, events, places, notes, and other app data remain available after refresh and across devices.
Find documents by exact details and similar meaning, then combine the strongest results.
Update shared boards, feeds, maps, polls, dashboards, and trends as saved data changes.
Ground a response in a focused set of documents already saved by the app.
Group information, surface patterns, compare choices, or suggest a next step.
Generate a useful result, show it to the user, and save it to MongoDB Atlas when the product needs it.
Upload: choose a non-sensitive PDF up to 20 pages and 3 MB. Each app accepts 25 PDFs.
Embed: the platform renders each page and embeds the page image with Voyage multimodal-3.5.
Retrieve: MongoDB Atlas Vector Search finds the pages most relevant to the question.
Generate: the built-in vision LLM reads those pages and answers with page citations.
Scans, charts, figures, tables, and selectable text work without OCR. At the kiosk, choose whether uploads are private to each browser or deliberately shared with everyone using the app. Never upload credentials, private company information, payment cards, government IDs, medical records, biometrics, or other sensitive files.
When starter data improves the experience, the builder writes 15 sourced public records to MongoDB Atlas. A user-created collection may start empty.
The initial deployed app cannot start with more than 100 records.
After launch, the app can grow through user input or user-triggered LLM actions, up to 500 records. The separate PDF allowance is 25 files per app.
The builder may research public information before the app is deployed.
The live app cannot run an open-internet search to collect new documents.
Users can add ordinary app data through the interface.
Users can trigger the LLM to create or transform a result and save it to MongoDB Atlas.