Activeloop
ActiveDatabase for AI
About
We provide a simple API for creating, storing, versioning, and collaborating on multi-modal AI datasets of any size. With Activeloop's open-core stack, you can rapidly transform and stream data while training models at scale. Deep Lake powers foundational model training by acting as a vector database with significant benefits, such as (1) the ability to use multi-modal datasets to fine-tune your own LLM models, (2) storing both the embeddings and the original data with automatic version control, so no embedding re-computation is needed (3) truly serverless service with no vendor lock-in. How cool is that? GitHub loves us - we're one of the fastest-growing libraries there, and we're used by little-known companies like Google, Waymo, and Intel. No big deal. Our founding team hails from places like Princeton, Stanford, Google, and Tesla, and we're backed by Y Combinator & other Silicon Valley heavyweights. Activeloop is hiring, and we want you! Check out our open roles on our YC page and join the fun. 10-min demo: https://activeloop.wistia.com/medias/aibvo0dst2 Whitepaper: https://www.deeplake.ai/whitepaper
Founders · 1
Founding CEO Activeloop, PhD on leave from Princeton, AI/ML, Data and Infra, Y Combinator S18, UCL 16’ Working on Data 2.0
Launch
Turn PDFs, images & tables into instant, cited answers. Agentic RAG that just works.
Activeloop announces Activeloop-L0, a multimodal, grounded RAG system that ingests unstructured data (images, PDFs, audio, spreadsheets) and provides cited, high-precision answers with an OpenAI-compatible API. It targets enterprise/go-to-market for organizations with large corpora, emphasizing no-ops infrastructure, scalable indexing, and on-premises data deployment.
Formerly “Snark.ai”, “Snark AI”
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