🔬Activeloop

Agentic Reasoning on Your Multimodal Data

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.

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Key Capabilities: Go Beyond Basic RAG

All thanks to Deep Lake, 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.

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

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.

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

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Get Started with Activeloop

Ready to unlock the true potential of your enterprise data?

1. Explore the Documentation: Dive deeper into concepts, architecture, and API references.
2. Try the Quickstart: Set up a basic instance and index your first multimodal data.
3. Talk to Us: Discuss your specific use case and see a tailored demonstration.