An extremely important functionality for agents is to simply extract fields out of PDFs. This turns out to be harder than people think because LLMs are primarily trained on predicting the next tokens. This leads them to "autocorrect" things that they shouldn't autocorrect. We launched an AI Extract capability that just excels at doing just this task with very high accuracy (95% vs 87% for others) and extremely low cost. Check out this blog on how we did it. The function can of course be called directly from SQL and be used throughout the platform.
https://t.co/60XC2mIZ2E
I got this question so many times today. "How can you grow 80% at $7B?"
The true answer is that we're finally seeing a breakthrough with AI agents starting to work in the enterprise.
The AIs have been super smart for a while, but have lacked basic context that's in people's heads, or in some SaaS system-or-record. A lot of organizations are deploying FDEs to capture this context, or Ontology, and feed it to the AI. This is labor intensive and expensive. We just automated that with Genie Ontology.
Once you have that enterprise context graph, an AI agent like Genie becomes magical. I find myself no longer waiting for answers from my CRO, CFO, CMO, CHRO etc, I just keep queuing up questions on the phone while sitting in meetings. It'd frankly addictive.
Our customers are starting to do the same, over 70% of all queries on the platform are now generated by Genie agents. This fuels more questions to the platform, which drives consumption, which drives revenue. That's the simple answer.
Today, we announced that we crossed $7B in revenue run-rate, growing over 80% year over year in Q2.
We also shared:
🚀 $100M+ revenue run-rate for Lakebase
🚀 $1.5B+ revenue run-rate for Lakehouse, growing over 100% year over year
🚀 Continued positive adjusted free cash flow
And we raised $5B in our latest fundraise.
We’ll use this capital to invest in:
1️⃣ Lakebase, our serverless Postgres database built for AI agents
2️⃣ Genie, our AI coworkers that actually understand your business data
3️⃣ Unity AI Gateway, our multi-AI governance solution that helps control costs
@iamVictorDey shares more in @Forbes:
https://t.co/y2LdxysBNi
Kimi K3 on @databricks via Unity AI Gateway, along with native K3 API access. Try it for very long-context work, cache-heavy agent workflows, or coding tasks.
Moonshot AI's latest open-weight model, Kimi K3, is now available on Databricks through Unity AI Gateway. @Kimi_Moonshot
Run Kimi K3 where your data already lives - governed, secure, and ready for custom AI apps and agents built with the data in your Lakehouse.
Unity AI Gateway lets you deploy Kimi K3 with enterprise-grade access controls. Govern every call, monitor performance, and scale securely across your AI apps. Test Kimi K3 alongside other frontier models without changing your application code.
The open-weight frontier just arrived on Databricks. Try it today. https://t.co/w0UPtVGYBU
More than 5,000 students applied. Now it's time to meet the inaugural Databricks Student Fellows.
Selected for their campus leadership and hands-on technical expertise, these fellows will bring data and AI learning to their universities through workshops, hackathons, mentorship, and community building.
Meet five standout fellows whose work spans AI research, data engineering, and developer communities →
https://t.co/hy0vUyzS4N