Hey Product Hunt! I’m Alan, founder of Collamn, and I’m excited to introduce an AI native workspace for working with databases. Collamn started from a simple frustration: 1. Working with data is still unnecessarily difficult. 2. You open a database client. 3. Search through connections, schemas, tables, columns, and relationships. 4. Write queries manually. 5. Switch to another tool to understand the result. 6. Then open an AI assistant and paste everything again because it has no idea what is actually happening inside your database. We wanted to remove that entire loop. Here’s the story: Be us. Try to build an AI agent that can accurately work with databases. Realize that the model is rarely the main bottleneck. The real bottleneck is context. An AI cannot reliably answer questions about your business when it does not understand your schemas, table relationships, previous queries, database tools, or the workspace you are currently using. So we built Collamn. 1. Connect your database. 2. Generate a graph of its schemas and relationships. 3. Ask a question in natural language. Collamn finds the relevant tables, opens the appropriate workspace, generates and runs the query, and presents the result without forcing you to jump between multiple tools. For example, you can ask: “How are sales performing this month?” Instead of only returning a block of SQL, Collamn can identify the relevant tables, open a query tab, execute the query, display the results, and explain what happened. You can inspect the generated SQL, validate the data yourself, and continue working from the same context. Collamn is not just a chatbot placed beside a database client. The workspace itself is agentic. The agent can understand your database structure, interact with tools, navigate the interface, execute tasks, and keep the result visible and verifiable for the user. Under the hood, Collamn combines: • A database knowledge graph for understanding schemas and relationships • An agent runtime for planning and tool execution • A secure LLM gateway for managing models, sessions, usage, and provider access • MCP connectivity so external AI coding tools and agents can work with the same database context • Familiar database workflows including table exploration, query editing, results, and connection management Our goal is to make Collamn the workspace where humans provide intent and judgment, while agents handle the repetitive execution required to understand and operate on data. Today, database tools show you what exists. Collamn is being built to understand what exists, help you decide what to do with it, and execute the work alongside you. We’re still early, and we would love feedback from developers, data teams, founders, and anyone who regularly works with databases. What is the most frustrating part of your current database workflow? Try Collamn today and let us know what you build.

Collamn

Give AI agents reliable database context

Give AI agents reliable database context