Incerto: The AI Copilot for Databases

This podcast introduces Incerto, an AI Copilot for databases, which aims to significantly boost developer productivity. This tool distinguishes itself from simple “Text-2-SQL” solutions by focusing on “Text-to-Task” functionality, allowing users to define complex database operations in natural language. Incerto emphasizes context awareness to generate accurate queries, manage schemas, and troubleshoot issues, supporting developers, DBAs, and data engineers. Key features include local operation for security, support for various cloud and local databases, and the ability to automate repetitive tasks while understanding the nuances of database structures.

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Frequently Asked Questions

What is Incerto?

Incerto is an AI-native copilot for databases, designed to transform natural language into database tasks. It leverages context-aware AI agents to perform actions like writing queries, managing schema changes, and moving data, aiming to significantly boost developer productivity.

How does Incerto differentiate itself from other AI database tools or “Text-to-SQL” solutions?

Incerto distinguishes itself by focusing on “Text-to-Task” rather than just “Text-to-SQL.” While SQL generation is a component, its core strength lies in its ability to gather and utilize extensive database context—including schemas, tables, indexes, workloads, and business use cases—to complete end-to-end tasks. It aims to overcome the limitations of existing tools that often miss crucial context or suffer from “context bloat,” leading to incorrect or inefficient outputs.

What kind of tasks can Incerto perform?

Incerto can handle a wide range of everyday database tasks. These include writing and optimizing queries, creating and modifying schemas, adding data, managing indexes, automating changes (e.g., splitting columns, validating data), and troubleshooting issues (e.g., investigating performance anomalies). It also supports adding business templates for auto-creating schemas, seeding data, or generating queries for APIs.

Who is Incerto designed for?

Incerto is built for anyone who works with databases, including developers, DBAs (Database Administrators), and data engineers. It’s particularly useful for teams looking to automate repetitive tasks like query writing and optimization, data movement, and schema changes. It addresses common frustrations experienced by users who find existing AI tools often lack the specific context needed for their unique database environments.

What are Incerto’s key security features?

Incerto prioritizes security by running locally, meaning no user data is sent to Incerto’s servers. The only outbound calls are to LLM (Large Language Model) providers. Database credentials are also stored securely through local hashing.

What database systems does Incerto currently support, and are there plans for expansion?

At the moment, Incerto supports Redshift, PostgreSQL, ClickHouse, and MySQL. The team has also integrated BigQuery for a client and plans to add more database support, at least with basic chatting capabilities, by the end of the current month.

How does Incerto handle complex challenges like schema drift and query optimization in real-world scenarios?

For schema drift, Incerto aims to keep its AI agents aware of changes as they happen, preventing them from working with outdated snapshots. However, the Incerto team acknowledges that this is a complex problem due to schema changes potentially originating from multiple sources. Regarding query optimization, while agents can assist, users are still expected to review, accept, and handle changes, similar to how one might interact with a code copilot like Cursor.

When was Incerto launched and what is its future vision?

Incerto launched in 2025 (according to the product info) on Product Hunt. The co-founders, Shiva Pundir and Yagyansh Bhatia, emphasized gathering user feedback to shape Incerto into a genuinely useful tool for developers, DBAs, and data teams. The vision is to provide an AI copilot that makes interacting and working on databases significantly faster and easier by focusing on deep, context-aware AI agents.

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