# Activeloop

<div align="center" data-full-width="false"><figure><picture><source srcset="https://3835530738-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGqXl9JlMh4d1ZO3BYxnA%2Fuploads%2FRLmePf4PhtDowIOcKBq9%2Fimage%20copy.png?alt=media&#x26;token=700c1d36-c9ef-4680-941d-ece1e83cc013" media="(prefers-color-scheme: dark)"><img src="https://www.activeloop.ai/static/diagram-e485c4122fbff8b5f58a7fe156588348.png" alt="" width="488"></picture><figcaption></figcaption></figure></div>

Activeloop-L0 is a compound AI system that ingests and answers questions from your unstructured, multimodal data. It delivers accurate, traceable answers with clear source citations, ensuring trust and transparency through visual reasoning.

[Quickstart](https://docs.activeloop.ai/setup/quickstart) or [Request a Demo](https://www.activeloop.ai/contact/)

## Key Capabilities: Go Beyond Basic RAG

All thanks to [Deep Lake](https://docs.deeplake.ai/latest/), Activeloop provides unique advantages over traditional RAG systems, enabling deeper understanding and control of your data.

💡 **Native Multimodal Understanding**: Leverage advanced Visual Language Models (VLMs) to intrinsically understand PDFs, PowerPoints, images, audio, and more **without** brittle OCR or complex pre-processing.

☁️ **Your Data, Your Cloud, Your Control**: Deploy entirely within your secure cloud infrastructure. Connect private data sources and bring your own models (BYOM), ensuring sensitive data never leaves your perimeter.

✅ **Trustworthy & Explainable Results**: Deliver highly accurate, grounded answers backed by clear citations directly to the source data, ensuring reliability, auditability, and user trust.&#x20;

<figure><img src="https://3835530738-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGqXl9JlMh4d1ZO3BYxnA%2Fuploads%2FfiS5i9nNHVJjef06BgI7%2FScreenshot%202025-04-24%20at%2010.18.07%E2%80%AFAM%20(1).png?alt=media&#x26;token=3fafa740-85de-4d98-bfbd-c76fdb9a58d8" alt="activeloop-l0 benchmarks"><figcaption><p>Activeloop-L0 achieves overall 84% state-of-the-art accuracy on 1,142 multimodal questions (292 PDFs, 5.5K pages). It outperforms text only RAG by +20%, visual RAG by +10%, and Alibaba’s ViDoRAG by +5% on their own ViDoSeek benchmark</p></figcaption></figure>

## Why Activeloop? Achieve Tangible AI ROI

🚀 **Accelerate & Automate Workflows**: Seamlessly embed deep knowledge retrieval and reasoning into core business processes like compliance checks, research synthesis, and customer support.

🧑‍💻 **Free Your AI Team to Innovate:** Eliminate the infrastructure bottleneck. We automate parsing, chunking, embedding, and indexing, letting your team focus on high-value AI applications, not data plumbing.

✨ **Unlock Actionable Insights**: Discover hidden connections and analyze trends across disparate data types. Extract meaningful insights previously buried in your complex multimodal data assets.&#x20;

## Use Cases Across Industries

* **Financial Services:** Analyze quarterly reports alongside market news videos and earnings call audio.
* **Pharma & Life Sciences:** Accelerate R\&D by connecting research papers, clinical trial data, and lab notes.
* **Technology:** Gain holistic customer understanding by correlating support tickets, call audio, and user session recordings.
* **Legal & Compliance:** Perform deep analysis across case law, contracts, and internal communications with full audit trails.
* **Insurance:** Streamline claims processing and enhance fraud detection by correlating claim forms, incident reports, damage photos/videos, repair estimates, and policyholder data.

### Overcoming Traditional RAG Limitations

Basic RAG struggles where enterprise needs are greatest:

* **Agentic Scaffold:** predefined loops and rigid agent scaffolds.
* **Multimodal Data:** Difficulty processing and relating information beyond plain text.
* **Infrastructure Burden:** High cost and effort to build/maintain complex pipelines.
* **Integration Challenge:** Difficulty embedding insights into meaningful workflows.
* **Control & Security:** Concerns over data leaving secure perimeters with SaaS RAG.

### Activeloop Knowledge Agents vs. Traditional RAG

| Feature                     | Traditional RAG                 | **Activeloop Knowledge Agents**          |
| --------------------------- | ------------------------------- | ---------------------------------------- |
| **Data Support**            | Mostly text-only                | ✅ **Native Multimodal**                  |
| **Reasoning**               | Limited keyword/semantic search | ✅ **Advanced relationship & multi-step** |
| **Integration**             | Basic retrieval                 | ✅ **Deep workflow integration**          |
| **Data Pre-processing**     | Manual/Complex (OCR often)      | ✅ **Automated / Native understanding**   |
| **Infrastructure**          | High complexity, manual mgmt.   | ✅ **Automated, streamlined**             |
| **Deployment/Security**     | Often SaaS, limited control     | ✅ **Your Cloud, Secure Private, BYOM**   |
| **Accuracy/Explainability** | Variable, opaque sourcing       | ✅ **High accuracy with clear citations** |

***

### Get Started with Activeloop <a href="#getting-started" id="getting-started"></a>

Ready to unlock the true potential of your enterprise data?

1. [**Explore the Documentation**](https://docs.activeloop.ai/user-guide)**:** Dive deeper into concepts, architecture, and API references.
2. [**Try the Quickstart**](https://docs.activeloop.ai/setup/quickstart)**:** Set up a basic instance and index your first multimodal data.
3. [**Talk to Us**](https://www.activeloop.ai/contact/)**:** Discuss your specific use case and see a tailored demonstration.
