Fragmented AI Data Creates Debt
The AI Data Plane for Continual Learning
Store every agent interaction, outcome, and artifact as a traceable stream; then retrieve, replay, and fine-tune continuously as your system evolves.
Database for AI
Trended #1 in Python
8.9K+Github Stars
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110+Contributors
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3.4K+Community members
Activeloop is SOC 2 Type 2 certified reinforcing its focus on secure and reliable AI data analysis for teams working with sensitive information.
Activeloop was named a Cool Vendor in the 2024 Gartner® Cool Vendors in Data Management: GenAI Disrupts Traditional Technologies Report.1
Up to 22.5% more accurate knowledge retrieval with RAG compared to basic vector search
Deep Lake adapts indexing to user queries, improving retrieval accuracy for AI data analysis. With fine tuning and querying, teams can access insights across millions of documents and accuracy improves with every search.
Cut data prep times by 50%, similar to Bayer Radiology or Matterport
Deep Lake’s open source format and pipelines speed up ML model training and dataset storage. Its tools for visualization, lineage, and natural language queries simplify using AI for data analysis as shown by teams like Bayer Radiology and Matterport.
What Activeloop can do
Multimodal search across formats
Search across text, images, videos, and audio in one step. You won't miss a detail just because it was buried in a chart, spoken in a recording, or hidden in an image.
This engine also lets you query unstructured data in SQL or plain language, so you can curate and refine datasets instantly.
Automated data indexing
Activeloop reads and organizes files automatically. No manual tagging or converting PDFs, videos, and other files into separate text documents.
Every dataset is versioned like Git, so you can see what changed, roll back, or branch off when needed.
Fast and accurate retrieval
Advanced indexing and querying deliver relevant, grounded answers even when searching across thousands of documents.
The built-in Tensor Query Engine means those answers come from curated, filtered data, improving speed and accuracy.
Built for AI data analysis
Designed for teams that need AI for data analysis, from sales and marketing to research, Activeloop supports secure and efficient work on complex datasets.
Visualizations of embeddings, lineage, and versions help you understand and improve your data over time.
How to use our AI data analyzer
Upload your files
Add PDFs, spreadsheets, videos, and recordings. No formatting is required.
Ask AI for analysis
Type a question in plain language. Activeloop searches and pulls information across all your files.
Get instant AI powered replies
Receive context‑aware answers grounded in your data, not guesses.
Who uses Activeloop?
Activeloop makes unstructured data usable and accessible.
RevOps & Sales Enablement Managers
Unify sales data across CRM, calls, and customer interactions. Quickly answer questions like "Which accounts mentioned pricing objections?" or "What insights can we use for our next QBR?"
Researchers and scientists
Look across papers, charts, and lab notes in one search. Ask questions and find answers even when data is split across visuals and text.
Lawyers and legal teams
Search contracts, scanned documents, and call recordings. Multimodal search surfaces the right clauses and saves hours of review.
Business analysts and knowledge workers
Find insights across reports, spreadsheets, emails, and recordings. Get quick answers to questions like "What was discussed about Q4 projections?"
Customer success teams
Get a holistic view of customer interactions across support tickets, chat, and calls. Answer questions like "What issues did this customer raise before renewal?"
Investigators and insurance adjusters
Search claims, photos, and reports in one place. Ask "Which claims mention storm damage?" or pull evidence faster for investigations.
Deep Lake, built for AI data analysis
Deep Lake is Activeloop’s open source database designed for complex, unstructured data. It keeps the benefits of a traditional data lake, like time travel, SQL queries, ACID transactions, and terabyte‑scale visualization. The difference is how it handles multi‑modal data. Images, audio, video, annotations, and tables are stored as tensors and streamed directly to queries, browsers, or machine learning models without slowing down GPU performance.
How Deep Lake fits into your LLM stack
Used by more than 100 data teams
“It's next-level. We've enabled a new human-machine interface that is natural to use and yields high-accuracy results for end-users. I am confident that adopting Activeloop was a value-add through improved innovation that will compound as our AI operations grow”
Principal Imaging Technology Scientist
Bayer Radiology“They started out with a vector store integration, so it's flown under the radar, but... @activeloopai's Deep Lake is an intriguing fully-fledged serverless data lake that supports attribute based filtering, multiple distance functions, MMR search.”
CEO & Founder
LangChainAI“As the datasets enlarge and become multi-modal, next-gen solutions built specifically to address those use cases, like Deep Lake, will help AI teams deliver models to production faster, and more efficiently.”
CTO – Enterprise Analytics & AI, Head of Strategy – Enterprise & Cloud Group
Intel“Downloading data every time you run an experiment is bound to break you and the training process. Deep Lake's on-the-fly streaming was an excellent choice for us: it was really easy to set up, and it started to bring the value from day one.”
Lead ML Engineer
Ubenwa AI“Davit & team are super responsive & hands-on with onboarding. Highly recommend the tool for managing large & complex datasets.”
Co-Founder
Dream 3D“Just needed to deploy a solution that works - and Activeloop made it simpler to ship our AI app quickly! The features that shined the most for me were the instant visualization in the Deep Lake UI, as well as fast data access Deep Lake format enabled.”
Director, Machine Learning
SDSC“A 100x speedup of Tensor Query execution for semantic search and question answering on legal documents. Deep Lake’s minimalistic architecture provided flexibility and light touch installation for our customers without introducing complexity such as adding a microservice. With Deep Lake’s ultrafast data loader, PyTorch was able to natively access the data and distribute it automatically across MPI workers, allowing for highly parallel embedding search.”
CTO
Hercules.ai“We've 10x-ed the time to production, increased model accuracy by 19.5% and reduced training costs by 32%, setting new standards in the last-mile delivery space.”
CEO
Tiny Mile“New models deployed in a matter of days instead of weeks.”
Director of Machine Learning
IntelinAirConnect data to AI easily(If you use Deep Lake)
Seamlessly connect your data to AI applications with Deep Lake's simple Python API.
1import deeplake
2from PIL import Image
3
4ds = deeplake.load('hub://activeloop/mnist_train')
5
6# Display an image
7Image.fromarray(ds.images[0].numpy())Visualize multi-modal AI data
Deep Lake datasets, including the embeddings, are visualized right in your browser or Jupyter notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on-the-fly, & stream them to your LLM of choice for fine-tuning
- Rapidly visualize different versions of your data
- Understand your data and improve its quality
- Query, train, & edit datasets with data lineage
Query unstructured data just like with SQL
Powerful query features to curate subsets in natural language. Tensor Query Engine allows you to query complex data fast and materialize it on-the-fly for subsequent training. Use familiar SQL syntax or natural language - chat with multi-modal data, at scale
Time-travel is possible. Data backups, too
Just like in Git, manage changes to datasets with simple commands. Visually inspect changes and revert anytime
Deep Lake is reshaping deep learning.
Dive into it.Drive revenue growth by shipping AI products faster, saving money by saving on GPUs, increasing data scientists’ focus on core business problems, & eliminating failed ML project risk due to the lack of a solid data foundation.
> pip install deeplake
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