Text2SQL.AI VS AskDoozy

AskDoozy's answer:

Doozy doesn't just answer questions in Slack, it goes and does the work. Mention @Doozy in a thread with a real task and it works in your own systems (your database, Stripe, GitHub, Linear, PostHog, WooCommerce, any MCP server, the web), then posts the result back in the thread with the files and a receipt for every action. It's built to be safe to point at production: every task runs in its own isolated sandbox that never holds your API keys or database credentials, database access is read-only, and tools that can delete or overwrite start switched off until an owner turns them on.

AskDoozy's answer:

Choose Doozy if you want an AI that finishes tasks in your own tools, not one that tells you how to do them. It costs one price per workspace with no seats or credits, so everyone in your Slack can use it, not only the people with a licence. Setup is self-serve: a non-technical team lead can install it and connect tools without an engineer or pasted API keys. And it's designed for teams who are nervous about letting AI near production. Credentials stay outside the sandbox, the database connection is read-only, risky tools are off by default, and you get a receipt for everything it did. You can try it free for 14 days with no card.

AskDoozy's answer:

TypeScript end to end, with Postgres for data, the Slack platform for the interface, and the Model Context Protocol (MCP) for connecting tools. Every task runs in its own short-lived, isolated cloud sandbox. Calls to models, tools and git pass through gateways outside the sandbox that attach credentials per request, so secrets never sit where the agent can read them. The marketing site is built with Astro.

AskDoozy's answer:

Support, customer success and operations leads at small and mid-sized companies (roughly 10 to 150 people) who work in Slack and usually have no engineering help on hand. They're the people who keep asking "can someone check this?" and waiting hours for an engineer to run a query: why a customer was charged twice, what happened to an order, how many accounts churned last month. Founders and engineers use it too, for investigations, reports and small code fixes that end in a pull request.

AskDoozy's answer:

At Spur, support kept asking in Slack "why did this customer see this?" and waiting a couple of hours for an engineer to run a query. In July 2026 I built a bot that could go and check itself. On its first day it found the root cause of a broadcast failure in four minutes. Support adopted it faster than engineering did, and people kept asking to connect more tools. Doozy is what I learned from that bot, rebuilt from scratch as a product any team can add to their Slack, with the safety work (no keys in the sandbox, read-only database, receipts) that running an AI against real production taught us we needed.

AskDoozy's answer:

• Spur
• FlowRipple