rawquery

A hosted data platform designed to be operated by LLM agents rather than humans. The product surface is a CLI that Claude Code, Cursor, or any other coding agent can invoke directly: connect a source, sync data, list schemas, run SQL, generate charts, publish results to a public URL. The human interacts in English; the agent handles the SQL and the plumbing.

What it does

Three moving parts:

• Source connectors. Stripe, HubSpot, and similar SaaS sources land as queryable schemas. In the demo scenario the user connects both with two CLI commands and gets ten tables across stripe_prod and hubspot_crm schemas within minutes.
• SQL execution. The agent writes and runs the queries. The post's example goes from "did my email campaign work?" to a cohort comparison table (847 email customers at 89.41 avg basket vs 1,994 control at 61.27) with no SQL written by hand.
• Publishing. One command turns a query result into a shareable public URL (https://rawquery.dev/c/<id>) with the chart rendered, no login required on the viewer side.

The pitch positions it against the traditional BI path: data engineer models the source, analyst writes queries, dashboard tool (Looker) hosts results, everyone waits for access. rawquery compresses that to one English sentence per question.

How it fits the LLM-agent workflow

Two design choices signal the target:

• CLI-first. The agent shells out. No MCP server, no web UI required, no auth handshake per query. This is the same reasoning behind mcp-vs-skills's case for keeping tool access simple and low-context.
• Schema introspection. list schemas and list tables with row counts are designed to be the first calls an agent makes, giving it the context it needs to write correct SQL without being told the data model.

See llm-as-average-democratizer for the philosophy behind the product — average SQL is good enough for most business questions, so remove everything between the question and the query.

Example usage

From the launch post average-is-all-you-need:

> connect my Stripe and my HubSpot to rawquery
[agent runs connector setup]
> I ran an email campaign in March. Did it move revenue?
  Compare people who received the email vs those who didn't.
[agent writes SQL, returns cohort table]
> Break it down by week. I want to see if the effect wore off.
[agent returns weekly breakdown]
> Save that, make a chart, give me a link I can send my manager.
Published: https://rawquery.dev/c/e3xW3uLXrGGheqYvZaEddw

The user wrote four sentences. The agent did the rest.

Limitations and open questions

• Correctness is on the user. The platform lets the agent write SQL fast; it doesn't verify the joins are right. For high-stakes analytics this is the cult-of-vibe-coding problem in analytics form.
• Closed source, hosted. Your data flows to rawquery's backend. For sensitive data this is a non-starter; compare titit-local-ai's argument for local AI.
• Connector coverage. The post shows Stripe and HubSpot only. Coverage breadth and freshness of sync will determine whether this is useful outside demo scenarios.
• Pricing, scale, query limits. Not disclosed in the launch post.

Related

• average-is-all-you-need — launch essay
• llm-as-average-democratizer — general framing of the philosophy
• ai-assisted-workflow — broader pattern of agent-plus-human workflows

Homepage: rawquery.dev. Free tier available via rawquery.dev/signup.