Hey š We're launching Velvet's new AI SQL editor today!
We made Velvet to solve our own problem. As an early-stage startup, we're built on top of Supabase, Stripe, and other tools. We needed an accessible way to unify data and run queries. Velvet lets any team member access real-time data, write complex SQL with AI, and turn those queries into re-usable components.
We've got an amazing group of early adopters already using it daily, and we hope you'll try it out!
š§š»āš» Connect databases, sync third-party APIs, and collect events
š® Ask natural language questions to write complex SQL with AI
š Save queries to share, visualize, or create API endpoints
Use Velvet to experiment, ship, and scale faster. Try it out at usevelvet.com, and share your feedback or questions below!
Use code TRYVELVET for 25% off after your free trial.
Congrats on the launch @emmalawler24
Writing queries is one of the best uses of AI. Many users of Github Copilot will be abe to appreciate the time saving that Velvet will bring.
I'm curious, how long did it take you to extract AI SQL as its own product for the public?
@emmalawler24@jgani We've been working on this platform for several months. Natural language to SQL is just one component of what we're doing. Additionally, we built a scalable and secure data platform to unify your data sources into one queryable interface. Besides the infrastructure, we focused a lot on the UX of the AI SQL editor itself, allowing you to iterate on your queries by simply asking another question, requesting a refinement, or editing it manually.
I haven't yet tried the new version but I've been impressed with how consistently the Velvet team has learned, iterated, and shipped since we first chatted a year ago.
I can see this approach replacing the request-export-spreadsheet workflow that hampers a ton of data-informed decision making for a lot of people.
- It's great for startups that don't have a full data team in place
- It's also great for larger cos with dedicated analytics engineering practitioners building data assets to be used downstream
The potential for enabling (and upskilling) power users is pretty exciting.
@thedatadavis - we're excited to have you try it out soon! Exactly, the IC power user is who we designed this product for. It boosts the productivity of engineers who can (1) move faster to ship new features and (2) experiment with collaborators on real-time data (often for the first time).
@atticusli Velvet works great for both teams and individuals. We want to make sure folks can get value on their own for solo projects or individual contributor work while also having a powerful way for teams to collaborate on data engineering and product development.
Congratulations on launch @emmalawler24 and @chris_hendel!
I've been beta testing Velvet for a couple of months, and I'm excited to see it go live.
Every startup reaches a point where they outgrow Amplitude or Mixpanel. These tools enforce specific data structures, and their SDKs get blocked by many browsers. They completely break with multi-tenancy and barely support organizations.
The best-in-class in-house analytics stack is Snowflake, Fivetran, and PowerBI. However, this costs about a quarter million dollars, and requires hiring a data team to maintain and analyze it. This stack can answer any question you want, but PMs lose the ability to easily ask questions directly.
Velvet bundles this warehouse/import/analysis stack, and sprinkles in amazing UX and AI to make it modern. Now, any startup can build an advanced data analytics stack that answers any question in their data, and its AI editor makes the product easy enough that PMs can self-serve questions - no data analysts required.
In the age of AI, controlling your data is key. Velvet centralizes your databases, third-party events (like Stripe), and even first-party events from your app - storing them in one place and letting you write queries that join across sources.
Thanks for all your feedback as an early adopter @philipithomas! We're a data platform for the modern tech stack - so your comparison to the Snowflake/Fivetran/PowerBI bundle makes a ton of sense.
Congratulations on the launch! Having all my data in one place and being able to query it easily sounds incredibly useful. And the fact that I can use an AI editor to make my data accessible and interoperable is just icing on the cake. Great job!
@michael_dawant1 - The AI interface is cool, but what's really unique is how we unify your data into one performant database. It's the first time most teams have been able to query their database tables alongside 3rd-party tools and events. Excited to have you try it out!
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