Solve
Specific problems, with a working snippet
One page per recurring search query — each answers the question with a working
<nlq-data> embed and names what nlqdb doesn't do for
that shape. Pages that hide their limits don't earn citations, and they don't
earn trust.
Solo builders
Founders and single engineers shipping side-projects on weekends. Spend day one of every project wiring up Postgres, ORMs, and migrations before the app does anything useful — they'd rather skip that step and ship.
-
How do I add a database to a side project without setting up Postgres?
If your side project needs a database but you don't want to provision Postgres, choose an engine, or wire migrations, drop one `<nlq-data>` tag in any HTML page — nlqdb mints the database, infers the schema from your first English query, and exposes the same data via SDK / CLI / MCP.
-
How do I add a leaderboard to a small product without writing SQL?
If your product needs a leaderboard, a top-N table, or a ranked list and you don't want to author SQL or wire a ranking ORM call, write the goal in English in one `<nlq-data goal="top players by score">` tag — the database, the schema, and the index decisions are all behind the element.
Agent builders
Engineers building LLM-powered agents that need to remember things across sessions. Structured memory has had no opinionated primitive — most teams stitch together a connection string, an ORM, and a hand-rolled migration loop before the agent's first tool call.
-
How do I give Claude or Cursor a SQL database it can create and query?
If you want Claude Desktop, Cursor, or any MCP host to have a SQL database — not just a connection to one you configured yourself — point it at nlqdb's hosted MCP server. The `nlqdb_query` tool provisions Postgres from the agent's first English goal (no connection string, no schema) and answers in English with the SQL shown.
-
How do I give my AI agent persistent memory across sessions?
If your agent needs to remember facts across sessions and later *aggregate* them, give it a real database via MCP — nlqdb's `nlqdb_query` tool provisions Postgres from the agent's first English goal and answers `GROUP BY` / top-N / per-period questions over what it stored. Retrieval gets you one fact; analytics gets you the report.
-
How do I run reports over what my AI agent remembered?
If your agent stores what it learns and you now need *reports* over that memory — counts, top-N, averages per group — point an MCP-aware agent at nlqdb and ask in English. It runs the `GROUP BY` in Postgres and returns rows plus the SQL. A vector store recalls one fact; a database answers 'top 10 this month.'
Analysts and PMs
PMs, ops, and customer-success leads who can write SQL but resent it. Live in Metabase, Retool, and Excel — want one-off questions answered without filing a data ticket, and want internal dashboards that don't charge per viewer.
-
How do I build an internal dashboard without per-seat pricing?
If you need an internal view over your data and per-seat tooling is out of budget, drop an `<nlq-data>` tag in any HTML page and ask for the report in English — no SQL, no schema setup, no per-viewer fee.
-
How do I run natural-language queries on a database without training a model on my schema?
If you want English → SQL on your data but don't want to maintain a training corpus or RAG layer, point `<nlq-data>` at your goal — nlqdb prompts directly from the live schema fingerprint, caches the plan, and shows the compiled SQL so you can verify before trusting it.
Have a problem we don't cover?
Email the search query you typed; if it matches a recurring theme in our ICP-mining cluster file, the page ships the same week. See also competitor comparisons.