AI models today are the smartest they've ever been - and they're starting to be smarter than some of us already. In short order, they will be in our products, our companies, and our lives - and they're already smart enough.
Why then do they still feel like interns? Why do we need to remind users to upload less and prompt better?
We're still stuck in chat interfaces, managing context windows and limited output lengths. The problem - heading our way like a brick wall - is data, and knowing how to use it. Put it another way, the problem is context.
Vector stores can't save us - neither can RAG. Unstructured pipelines throw contextual information out the door in chunks, making it impossible to do comprehensive, precise retrieval.
If you have great ingestion, you can get structured, high quality retrieval at sub-millisecond speeds without hallucinations, and a lot more becomes possible.
This is why we're building Skewless - the first agentic data layer. We've building it because we need it - both as a company and as the people in it.
Do you wish data was easy and lovable? Skewless.
Does something die inside of you when you chunk and embed tables? Skewless.
Do you have a datalake of shame, or a folder with a thousand scripts for ELT? Skewless.
Don't know what structure your information will be except in production? Skewless.
As a team that has built hundreds of enterprise ETL pipelines for large enterprises, and the first agentic retrieval system, we understand both sides of this problem - and the sheer scale of it.
Waitlist coming soon.
