🔬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.

Quickstart or Request a Demo

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

---

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.