PostgreSQL Assistant for Slack | Natural Language to SQL Chatbot

Turn your PostgreSQL database into a conversational AI data analyst in Slack. This template creates an intelligent natural language to SQL assistant that helps your team get instant answers from your data without writing queries. Perfect for self-service analytics across your organization!

What this PostgreSQL agent does

• Converts natural language questions into SQL queries automatically

• Answers data questions in natural language

• Acts as an AI SQL query generator for your database

• Generates publication-quality charts and graphs

• Explains findings in clear, actionable language

• Enables self-service business intelligence for non-technical teams

Examples of questions this assistant can answer

• "Show me our top 10 customers by revenue this quarter" (sales analytics)

• "What's our user retention rate over the last 90 days?" (product analytics)

• "Compare website traffic sources month-over-month" (marketing analytics)

Who this agent is for

• Teams with a PostgreSQL database but limited analyst bandwidth

• Organizations seeking business intelligence automation and self-service analytics

• Marketing, sales, and operations people who need quick answers without waiting for the data team

• Companies wanting to democratize data access with conversational analytics

• Anyone who wants an "always-on" AI data analyst in Slack

Why use an AI-powered data assistant?

Eliminate bottlenecks in your data analytics workflow. Instead of waiting hours or days for SQL queries, team members can ask questions in plain English and get immediate, accurate results. The text-to-SQL automation means anyone can access insights, not just those who know SQL.

How to set up this agent

Here's a video walking through how to set up this agent. (This video shows Google BigQuery instead of PostgreSQL, but the setup is otherwise the same.)

1. Authenticate with PostgreSQL and Slack.

2. Document your schema: replace the placeholder table documentation with your own. One tip for doing this is to ask the agent: "Explore our tables and summarize the database structure so you can efficiently retrieve data in the future."

   • List your key tables (start with 5-10 most queried)

   • Document important fields and their types

   • Map relationships between tables

   • Define business metrics and terms

3. Add common query patterns: include 3-5 SQL templates for your most frequent questions.

   • Daily/weekly metrics

   • User activity

   • Revenue breakdowns

   • Funnel analysis

4. Add this agent to your Slack.

Additional ways to customize this agent

• Connect more tools: give your agent access to other tools you use internally, like Google Docs, Google Sheets, Linear, etc.

• Change the default color palette for visualizations: provide hex codes for your own brand colors in the "Visualization Guidelines" section of the instructions.

• Give the assistant a name and personality: give your assistant a name, if you'd like.

💡Tips for success

• Start small: document your top 5 tables first, expand later.

• Include examples: show the assistant what good queries look like.

• Define metrics clearly: "revenue" means different things to different teams.

• Add context: note which tables are indexed, which fields are sensitive.

• Iterate: add more documentation as you see what questions people ask. Don't be afraid to ask the agent for more ideas on how it could be improved!