Activeloop-L0: State-of-the-Art RAG Accuracy on Your Data

While working on Deep Lake, I have seen many RAG systems collapse when exposed to production-scale corporate data. They often rely on predefined loops, custom logic and rigid agent scaffolds. Activeloop-L0 provides your agent with highly precise and answers grounded on your multimodal data.

Why can’t we reliably analyze corporate documents?

• Architectural hurdles: messy data integrations, unexpected infra costs, and reliability/safety constraints.

• Commodity RAG lacks depth for multimodal enterprise data (documents, images, audio).

• Infrastructure burden: parsing, chunking, embeddings, indexing, vector DBs, and agent loops slow teams.

But wait, is RAG still relevant despite large context models?

Let’s consider four extensive NASA documents [1, 2, 3, 4], each between 80 to 100 pages, containing visual descriptions, and pose a highly complex question.

ChatGPT with o3, despite having full PDFs in context, failed after 11 minutes of reasoning. Now, imagine you have thousands of corporate documents that can’t be contained in a context. In contrast, Activeloop-L0 provided the correct answer in 4 minutes and can scale to a million documents.

What is Activeloop-L0?

Activeloop-L0 is a compound AI system that ingests your unstructured data and returns grounded answers. Behind the scenes, Deep Lake indexes neural representations at scale, then fuses “thinking tokens” with high-precision retrieval for fast multi-hop reasoning.

It is available on chat.activeloop.ai now.

How is it different compared to a traditional RAG?

• Multimodal: Built-in support for images, PDFs, audio, and spreadsheets.

• Integrated Reasoning & Retrieval: Eliminates the need for loops.

• Deep Indexing: Cost-effective multi-layer indexing for richer context early on.

• Simple: Focus on innovation, not maintaining infrastructure.

• Grounded and Accurate: Clear citations for trustworthy insights.

How accurate is Activeloop-L0?

Activeloop-L0 achieves overall 85.6% 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 +6% on their own ViDoSeek benchmark.

Is there an OpenAI-compliant API?

Yes, Activeloop-L0 is available with an OpenAI-compliant API. You can easily plug into your agents for providing highly relevant context. You can get started here. https://docs.activeloop.ai/setup/quickstart

Read to Deploy on Your Data?

Activeloop is trusted by F500 including the likes of Bayer, Flagship Pioneering, Matterport.

• Your Cloud: Deploy on your cloud, ensuring data never leaves your infrastructure.

• Your Models: Integrate your LLMs.

• Your Security: SOC2 compliance, fine-grained access control, and SSO.