Here’s what I tested and liked
1. Easy configuration of hosts and switching between them. Added and modified some dbs and the tool was able to catch that.
2. Step by step resolution of queries. Tested this with the query you provided and it navigated well through the 109 tables and gave a detailed approach. I saw the agent encountering problems in the middle and resolving it during execution. The sql editor, chat window with descriptions around each query, and the final TLDR answer response is amazing.
3. Entire resolution took less than a minute. On checking each table that it went through, I’m fairly certain I would have spent a lot of time analyzing and writing queries.
4. The chat window has all the right tools to manage context. I liked how it detects and adds context automatically.
Incerto
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!
Voicely
@shivapundir Good luck
Incerto
@mustapha_ajermou1 Thank you :D
ScaryStories Live
@shivapundir Congrats Shiva, love the “text-to-task” framing. How do you see Incerto fitting into DB workflows—daily copilot or a power tool for troubleshooting?
Incerto
@imnikhill10 Thank you ❤️
@shivapundir Congratulation on the launch.
Incerto
@porush_puri Thank you ❤️
@shivapundir Congrats on the launch, Love the ‘Cursor for databases’ positioning — the focus on true context instead of just text-to-SQL makes a ton of sense. Super curious about how Incerto handles schema drift and query optimization in real-world messy DBs. Excited to try it out!
Incerto
@nishant_dubey2 On schema drift, the idea is to keep the agent aware of changes as they happen, so it doesn’t get stuck on outdated snapshots (although this is a hard problem in itself, as changes to the schema can come from multiple sources). For query optimization, the agents can't automate everything. Just like Cursor you would be required to accept and handle changes.
Incerto
Hey PH 👋
I’m Yagyansh, Co-Founder of Incerto. Super excited to be here today for our launch!
While Shiva shared the what and why of Incerto, I’ll keep this short: I’m here all day to answer your questions, brainstorm use cases, and hear your thoughts.
Your feedback means a lot to us—it’s how we’ll shape Incerto into something genuinely useful for developers, DBAs, and data teams.
Would love your support and can’t wait to hear what you think 🙏
@whybee99 Congrats on the launch, Super excited to see how Incerto handles complex real-world schemas without bloating context. Do you plan to support integrations with cloud DBs (e.g., Snowflake, BigQuery) early on, or focusing on local DB workflows first?
@whybee99 @nishant_dubey2
We do support Redshift, Postgresql, Clickhouse and Mysql at this moment.
Funnily enough we have integrated BigQuery for one of our clients just last week. We will add more databases by end of this month -- at least with basic chatting capability.
@whybee99 @anurag_pandey19 great!
@whybee99 Congrats on the launch! Seems promising!
Incerto
@varun_kedia Thank you!!
Incerto
@varun_kedia Thank you ❤️
@whybee99 Looks very exciting. All the best brother!
Great initiative and promising product scope. Congratulations team on the launch.
Qq: how do and upto what extent you guardrail prod data not to mess things Up by agentic hallucinations
Incerto
@debarghyaroy every single SQL request is routed to the user and no request is executed on its own. AI has dummy tools which just send a request to the user, and actual executions are done by user on UI.
Even in "auto" mode, all auto requests are sent with a read only mode, and mutable queries require user approvals
@debarghyaroy Thanks.
That's in important one, we have it summarized in this blog : <blogs in website, PH is not letting me post link>
In short AI doesn't even use MCP, it has not way to execute queries at all. Identity and credential of data is completely dependent on user.
You always have to accept the query (which is visible and write queries are marked with bold red color).
You can use readonly user in credential and then there is 0 chance any thing bad happens.
For PII data you can obsfucate collumn with configurable name. E.g you can say "mobile", "email", "Name" should never go to AI and we obsfucate it before sending to LLMs.
@anurag_pandey19 @whybee99 thanks for sharing the insights , does it mean , you relives the Dev by helping to write queries and form schema architecture , is that my understanding correct ?
if my understanding is correct how it is different than anormal query anyonce ask while building with AI ?
lets say i am using claude for my dev , it can also give me same capability is that correct ?
and if that understanding of mine is correct , how do you see , how can we actually make it in AUTO Mode? DO you think of auto in replication of sharding or probably pre prod in place to get it done by iteslf, compare the prod and pre prod , analyze differences , figure out ambiguity as a whole and finally sends out probably 1000s of DB ops in summary to user for approval something of that short ?
@whybee99 @debarghyaroy
Auto mode is just for readonly queries (which executed with setting readonly = 1 for clickhouse, as an example)
If you are comparing prod and pre prod, and you want to analyze along the way, you'd not use auto mode.
It will make first SQL query -> ask permision to run -> slap the result (truncated and obsfucated) LLM -> come back with insights -> Another SQL query -- this loop goes on.
It can also transfer to more suited agent in the loop.
If you are in auto mode, you don't have to click "accept" every time, that's the only convinience -- and for write queries there is no auto mode.
All value is in context management and query execution loop and ability of agents to explore database without you having to tell which database, table, etc to query.
100s of DB ops -- doesn't happen. It will not do 1000 queries one by one ever. It has limit of 20 consecutive query without user interaction.
Its free to use from website please use it once when you have time, and that should clear lot of doubts.
Let me know if I answered your queries, happy to clear further doubts.
@anurag_pandey19
Thanks Anurag, i understand , context and READ queries are normal for Any LLM no and they are also agentic ?
i was just trying understand how it differentiates between Incerto and normal LLM agentic calls?
and what i was saying is there any line of thoughts or scope that you guys are planning , which actually can help a TON that preprod , streaming , batching soltn. 1000s of ops , then You are unstoppable along with if you can also get into all CRUD ops.
Congrats on the launch, Shiva 🚀 Love how you’ve built Incerto with context-awareness at its core. I feel that’s usually the missing piece when AI meets databases.
We’ve seen something similar on the strategy side with Escape Velocity AI: without the right context, outputs feel shallow or generic. Once you layer in structure and domain-specific logic, it changes the game.
Do you see early adopters using Incerto more for day-to-day query writing, or for heavier schema-level work?
@andreitudor14 We see it majorly for day-to-day queries and generating database related code.
Schema level works are rarer in comparison.
Production issues are rare, but when that happens, our customers just use our tool to solve it. It is has all the context around firing issues (deterministically tracked)
@anurag_pandey19 That makes sense, as day-to-day queries are where the time savings really add up. Interesting that you’re also covering production issues when they come up, since context-tracking there feels like a real differentiator. Do you see teams trusting Incerto as a primary tool over time, or more as a companion alongside their existing workflows?
@andreitudor14
Its primary tool for all our clients, they are doing north of 20 Million tokens a day -- in just 3-4 people team.
There interaction is constantly through 10 am - 6 am, meaning they are sort of using it as we use VSCode or cursor. This is for generating queries for professionals who do this very often.
If a business head is using Incerto analysis they are not gong to spend more than 6 hours on product a week.
What does AI-native mean? Isn't it more like being pregnant, either you are or you are not, there is no pregnant-native. 😅
@michacassola
I can tell you why we use "AI Native", can't debate the around literal meaning. Product has been built around the intelligence (AI) as it's core. Every decision we make is affected by that, because at the end the core flow is LLM powered. Every new feature we add, which even can be used without AI has context tagged along with it, so that its LLM comprehensible (formats, structure, core prompt for what a piece of context represents matters).
We would have just called it AI Database Copilot, but the need to differential from "Sprinkle of AI" vs "Built around AI" because around Dec 2024 our product was 90% deterministic with some AI powered feature, but later we re did the whole thing when Claude got so good, and we realized that AI making the decision and asking user for approval is better than deterministically suggesting and then use AI to modify.
Time spend on figuring out prompts, contexts, evaluating the outputs and worst case scenario is more than time writing code.
Hope it makes sense.
No comments on pregnant-native part.
@anurag_pandey19
What pun?
Ok: AI-native = Mostly AI
😀
@michacassola yeah, core is AI. AI gets better our products core value automatically improve.
Astra Cloud Vulnerability Scanner
Context is everything! Also love that Incerto runs locally, eliminating most of the security concerns.
Congratulations on the launch @shivapundir 🚀
@ujwal_15 , yep! Thanks for commenting about it.
Although its just not the local part which makes it secure, we have given AI no access to execute anything on the databases. It can just "wish to route".
More on this here : https://incerto.in/blogs/safe-co...
Incerto
@ujwal_15 Thank you for the support ❤️
The blog shared by Anurag dives into more details :)
Lancepilot
Congrats on the launch of Incerto. Wishing you huge success ahead an AI copilot for databases sounds like a real productivity booster. Excited to see how it transforms the way devs handle queries and data management.
Incerto
@priyankamandal Thank you! Would love to get your feedback and thoughts!
Incerto
@priyankamandal Thank you ❤️