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.
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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.
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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.
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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.
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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.
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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.'
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How do I store and query my chatbot's conversation history?
If your chatbot needs to keep its conversation history and answer questions like 'messages per day' or 'most active users this week', give it a real database. nlqdb provisions Postgres from your first English goal and runs the GROUP BY in SQL — a vector store recalls one message, a database counts them all.
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How do I track and query my AI app's token usage and cost per user?
If your LLM app needs to track token usage and cost — per user, per model, per day — log each call as a row and ask in English. nlqdb provisions Postgres from your first goal and runs the GROUP BY in SQL, so 'spend per user this month' is a real query, not arithmetic over a JSON log.
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How do I log my AI agent's tool calls and query which tool fails most?
If your agent calls tools and you need to know which tool fails most and how slow each one is — log every tool call as a row and ask in English. nlqdb provisions Postgres from your first goal and runs the GROUP BY in SQL, so 'error rate per tool' is a real query, not a grep over traces.
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How do I log my RAG retrievals and query which sources get used most?
If your RAG agent retrieves chunks and you need to know which sources get used most — log each retrieval as a row and ask in English. nlqdb provisions Postgres from your first goal and runs the GROUP BY in SQL, so 'retrievals per source this week' is a real query, not a scan over a vector-store log.
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How do I track and query my LLM eval scores across prompt versions?
If you run LLM evals and need to know which prompt version regressed — log each scored case as a row and ask in English. nlqdb provisions Postgres from your first goal and runs the GROUP BY in SQL, so 'pass rate per prompt version this month' is a real query, not a spreadsheet pivot.
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How do I safely give an AI agent database access without it running dangerous SQL?
If you want an AI agent to use a database without handing it a connection string and hoping it never emits a DROP, nlqdb keeps the agent on the data side of a trust boundary: writes are server-built parameterised inserts, read SQL passes a fail-closed three-stage validator, Postgres RLS isolates every row, and the compiled SQL is always shown.
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How do I give multiple AI agents shared, persistent memory?
If you want a crew of agents to share one memory instead of each keeping its own, nlqdb gives them a single Postgres they all write to with `nlqdb_remember` and recall in English — every row tagged with the agent that wrote it, so you can roll the team's memory up per agent.
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.
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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.
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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.