A real persistent workflow
The app uses MongoDB Atlas as its database. Its primary journey visibly creates or changes a record, reads the saved result back, and keeps shared data after refresh.
A WORKING, DEPLOYED PRODUCT
WHAT YOUR APP
CAN DO.
Every app has a MongoDB Atlas database backend, an LLM selected by the builder, a responsive interface, working controls, and a production URL.
CORE INGREDIENTS
Every delivered app must include all three.
AI grounded in the app
The builder chooses an LLM for the job. A clear user action runs it on saved app data, shows the result, and can save that result back to MongoDB Atlas.
Tested and deployed
The result has a responsive interface, working controls, useful loading, empty, success, and error states, browser verification, and its own production URL.
OPTIONAL FUNCTIONALITY
Add only the ingredients that serve the idea.
Find and recommend
- Keyword search
- Meaning-based and hybrid search
- Image similarity search
- Comparisons and recommendations
Understand and create
- Summaries, explanations, classification, and writing
- Image upload and phone camera
- AI vision grounded in saved images
- Supported document upload: PDF RAG with page citations · 20 pages and 3 MB maximum
See what is changing
- Maps, weather, and optional location
- Live boards, feeds, and polls
- Dashboards and aggregations
- Charts and time trends
Turn data into a result
- QR codes
- Scheduled record actions
- Confirmed multi-step workflows
- Sharing and downloads
DATASET LIFECYCLE
The dataset is created during the build—not by the live app searching the web.
1
Build time: the trusted builder researches allowed public sources, preserves each source URL, and writes the initial dataset to MongoDB Atlas before deployment.
2
Launch: the deployed app reads and searches the Atlas records that were created during its build.
3
No runtime web search: after deployment, the app cannot crawl the web, search the open internet, call third-party sites, or refresh its dataset from web results.
4
How data can grow: a person can enter or edit records in the app. A user-triggered LLM action may create a result from information already saved in the app, and that result can be saved to Atlas.
VALIDATED STARTER THEMES
Five focused projects that fit this runtime.
Ask a small document and cite the page
Live flow: upload a non-sensitive PDF up to 20 pages and 3 MB, wait while page images are embedded with Voyage multimodal-3.5, then ask a question. MongoDB Atlas Vector Search retrieves the relevant pages and the built-in vision LLM answers with page citations.
Boundary: 25 PDFs per app. Scans, charts, tables, and selectable text work without OCR. At the kiosk, choose private-per-browser uploads or an intentionally shared library.
Turn a failure into a reusable case
Build-time dataset: sourced public debugging cases and failure patterns are researched and stored in Atlas during the build.
Live flow: add a redacted failure, find similar saved cases with hybrid search, then request and save an LLM-generated next step.
Boundary: no credentials, automatic trace ingestion, observability connection, runtime web search, or autonomous repair.
Save, rediscover, and compare agent patterns
Build-time dataset: sourced public agent patterns, examples, and tradeoffs are researched and stored in Atlas during the build.
Live flow: add a pattern, search the saved library by words and meaning, select two, then request and save an LLM comparison.
Boundary: no runtime web search, arbitrary agent execution, or MCP invocation.
Find a relevant event from known listings
Build-time dataset: current public event listings, dates, venues, topics, locations, and source URLs are researched and stored in Atlas during the build.
Live flow: search those saved listings, filter by date or nearby location, add an event manually, then request and save an LLM shortlist.
Boundary: the live app cannot web-search for new events or refresh listings automatically.
Turn project updates into a current readout
Build-time dataset: sourced public release milestones provide an initial Atlas dataset created during the build.
Live flow: add a milestone or blocker, watch the shared board update, view status and time trends, then request and save an LLM summary.
Boundary: no runtime web search or automatic GitHub, Jira, or Slack ingestion.
Validation means each scoped prompt passes the event guardrail and complexity checks and maps only to APIs implemented in the generated-app runtime. It does not mean these apps may claim unsupported integrations.
BUILD ASSISTANT
Ask how to build your idea.
See how the data and LLM fit together.
MONGODB ATLAS + AI →Features remain bounded for a short event build. Oversized ideas are reduced to a complete version that can be tested and deployed.