Incerto is an AI-native Copilot for databases. Using context-aware agents, it turns natural language into tasks — like writing queries, schema changes, or moving data — boosting dev productivity 10x with 90% less manual work.
Shiva here, Co-Founder of Incerto. Think of Incerto like "Cursor for databases" — a copilot to interact and work on databases.
🚀 Why are we building this?
There’s no true AI-native database tool built from the ground up with AI Agents at its core — and the few that exist still lack the right context to generate correct queries.
Working with a database isn’t just about connecting AI — it’s about understanding schemas, tables, indexes, workloads, and business use cases.
Current tools either miss real context or suffer from context bloat.
We’re changing that — because CONTEXT IS EVERYTHING (and we take care of it).
🔧 What can you do?
Perform everyday database tasks — from writing and optimising queries to creating schemas, adding data, and managing indexes.
Automate changes e.g., split a column into a new table, validate data, and build the right indexes.
Troubleshoot issues e.g., investigate "Why is memory high between 9–10 AM every day?"
Speed up workflows, add business templates to auto-create schemas, seed data, or generate queries for APIs.
In short: everything you do with a database today, just faster and easier.
👩💻 Who is it for?
Developers, DBAs, Data Engineers — anyone who works with databases.
Teams that want to automate repetitive tasks (write/optimise queries, move data, change schemas etc).
Anyone who’s said things like:
"Why is this AI creating a `users` table? Our `users` table is completely different."
"I just wanted to split a column, why did it break my schema?"
"I copied this query from ChatGPT, but it doesn’t even run on my DB."
🤔 "Is this just another Text-2-SQL?"
Nope. SQL generation is only one small part. In fact, we like to call it "Text-to-Task":
You define a task in natural language.
Our agents gather the right context.
They collaborate to complete the task end-to-end.
🔒 Security Concerns?
Runs locally — no data sent to our servers.
Only outbound calls are to LLM providers.
Database credentials are stored securely (hashed locally).
@whybee99@anurag_pandey24@vimal_raj_r Really interesting idea - most of our DB stuff is without any AI intervention - how does this work with Mongo / Vercel / Tinybird out of curiosity
ncerto looks amazing! Boosting dev productivity 10x with context-aware AI is huge. Best of luck with the launch!
What a game-changer that sounds! 🚀 With that degree of automation, teams may save countless hours by converting simple database activities into sophisticated ones. I'm curious if Incerto is primarily concerned with execution or if it also manages query optimization and recommends best practices.
Congrats on the launch!
I really like the idea of being able to just ask about the database and automate everyday tasks. One thing I’m curious about is how does Incerto handle cases where the schema is messy (e.g., columns named temp1, temp2) and most of the real context is hidden in the data itself? Essentially, in situations with dirty legacy codebases, does Incerto crawl the database to build that missing context, and how accurate is it in practice?
Congrats on the launch, @shivapundir and team! The idea of a database copilot with proper context sounds really useful. I’ll give it a try soon.
Fantastic product—here’s my take:
Promise: To create a 'context-aware' database co-pilot that unifies and automates database change management, troubleshooting, and workflow optimization for developers and data professionals. Did I get it right?
In that case, I think the primary risk is the ambiguity of 'context-aware,' which could lead to a 'jack-of-all-trades, master-of-none' solution in a crowded market of highly specialized, best-in-class tools for schema migration, observability, and querying.
If so, my recommendation will be proceeding but narrowing the focus to validate a single, high-pain workflow for a specific persona. For example, focus exclusively on 'performance troubleshooting for application developers who are not database experts' to prove the 'context' advantage is a 10x improvement over existing fragmented solutions.
Incerto is an AI-native copilot for databases, just describe what you need, and context-aware agents handle the rest. From writing queries to schema changes and data moves, it slashes manual work by 90% and boosts dev productivity 10x.
About time.
Sounds like this is mostly for DB end users (business analysts, etc).
Not sure if there are similar tools for developers but have been doing it indirectly via Copilot on VSCode.
For full stack/backend+DB developers integration with the most commonly used development IDE would be ideal.
Congrats on the launch, Shiva! Really like the “text-to-task” idea, feels way more practical than just text-to-SQL. What’s been the trickiest part of making the agents actually get the database context right?
Shiva pandey all the best .. So proud of you guys.. Really done a great job.
The product looks great. Looks like modern day MySQL workbench with AI features.
Hey guys, congratulations on the launch 🔥 Have you tried any other AI tools on your DB?
Cngrats on the launch, @shivapundir and team 🎉! Love how you’re positioning Incerto as ‘Cursor for databases’ , especially with the focus on context, which is usually the missing piece in AI-first database tools.
Curious to hear your thoughts on how Incerto handles database-specific context at scale.
🚀 This is a game-changer!
I’d love to see AI agents optimize database queries automatically and give smart recommendations for schema changes.
Also, being able to ask natural language questions about data would be 🔥.
Congrats to the team, this is definitely a must-try tool for data professionals! 👏💡
Hey 👋 everyone!
Shiva here, Co-Founder of Incerto. Think of Incerto like "Cursor for databases" — a copilot to interact and work on databases.
🚀 Why are we building this?
There’s no true AI-native database tool built from the ground up with AI Agents at its core — and the few that exist still lack the right context to generate correct queries.
Working with a database isn’t just about connecting AI — it’s about understanding schemas, tables, indexes, workloads, and business use cases.
Current tools either miss real context or suffer from context bloat.
🔧 What can you do?
Perform everyday database tasks — from writing and optimising queries to creating schemas, adding data, and managing indexes.
Automate changes e.g., split a column into a new table, validate data, and build the right indexes.
Troubleshoot issues e.g., investigate "Why is memory high between 9–10 AM every day?"
Speed up workflows, add business templates to auto-create schemas, seed data, or generate queries for APIs.
👩💻 Who is it for?
Developers, DBAs, Data Engineers — anyone who works with databases.
Teams that want to automate repetitive tasks (write/optimise queries, move data, change schemas etc).
Anyone who’s said things like:
"Why is this AI creating a `users` table? Our `users` table is completely different."
"I just wanted to split a column, why did it break my schema?"
"I copied this query from ChatGPT, but it doesn’t even run on my DB."
🤔 "Is this just another Text-2-SQL?"
Nope. SQL generation is only one small part. In fact, we like to call it "Text-to-Task":
You define a task in natural language.
Our agents gather the right context.
They collaborate to complete the task end-to-end.
🔒 Security Concerns?
Runs locally — no data sent to our servers.
Only outbound calls are to LLM providers.
Database credentials are stored securely (hashed locally).
✅ How to try it?
👉 Download from our website - https://incerto.in/
🎥 Watch the installation demo - https://youtu.be/AZx8VrAP838
💬 Ask us anything below, we’ll be here all day.
Got a use case in mind? Drop it in comments, we’ll help you build it.
🙏 Thank you, Product Hunt community!