The Multimodal Lake House : Partnering with Lance

Remember when you took a family photo & Ghibli-styled it?

Or that vibe coding session, when you pasted a screenshot of the browser so the AI can help you debug some Javascript?

Today, we expect AI to be able to hear, see, & read. This is why multimodal is the future of AI.

Multimodal data means using text, images, video, sound, even three-dimensional shapes with AI.

These are magical user experiences. But they aren’t easy to build. Data pipelines must be built to manage larger files. Embeddings need to be extracted from these unstructured files in ways that don’t explode compute costs.

Multimodal data is orders of magnitude larger than text : the average PDF is 10x larger than a text file & a YouTube video is roughly a million times larger.

Plus, multimodal data doesn’t change one part of the data pipeline : engineers must process the data at each step of the AI stack, from model training to real-time serving & downstream analysis at petabyte scale.

We kept hearing about these problems from builders & in the same breath, about a company that solves them.

Founded by Chang She, creator of the Pandas Library, & Lei Xu, core contributor to HDFS, the Hadoop file system, LanceDB has a tremendous heritage within the data ecosystem.

RunwayML, Midjourney, WorldLabs, ByteDance, UBS, Harvey, & Hex use Lance. We admire the technology so much, we are using it internally at Theory as part of our AI stack & we’re excited to partner with Chang & Lei to bring multimodal AI to builders & users everywhere.

Read more about the multimodal lake house & the kinds of AI it can enable.