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Database for AI
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
Activeloop provides a continual learning infrastructure for AI, centering on its Deeplake GPU database and related tools to make AI agents smarter and cheaper through a continuous learning loop. The platform integrates data, memory, and software in a unified stack to support production deployment and continual improvement.
Deeplake is described as a GPU-native database that stores vectors and tensors in one store, enabling rapid AI agent grounding and query capabilities. The stack includes Hivemind (memory) and Loop (refinery/software factory) to capture production traces, remember trajectories as shared memory, and improve cycles through verification before shipping. The software factory turns feedback into production via continuous learning, with a four-step loop: observe, remember, improve, verify. The offering is positioned as infrastructure for continual learning across data, memory, and software, deployable on surfaces your team already runs on.
Who it’s for: Enterprises and startups deploying AI agents and data pipelines that require continual learning, model improvement loops, and grounded AI with production traces and shared memory. Industries referenced include MedTech, Robotics, and Biotech/biomedical research.
Customers: Bayer, Matterport, Flagship Pioneering, Fortune 500, MedTech
Hiring/Career opportunities mentioned on site; references to deployments in enterprises and startups; pricing moved page indicates product-market activity
Founding CEO Activeloop, PhD on leave from Princeton, AI/ML, Data and Infra, Y Combinator S18, UCL 16’ Working on Data 2.0
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.
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