Harness the Power of Generative AI Connected to Your Internal Data

The Power of LLMs and RAG to Transform Information Retrieval and Knowledge Generation

RAG (Retrieval Augmented Generation) extends the capabilities of LLMs by integrating specific information from your company, such as databases or documents, to enrich the context.

Advanced RAG Performance

Advanced RAG far surpasses other technologies in terms of performance: simple RAG achieves a CoT (Chain of Thought) accuracy score of 20%, while advanced RAG reaches 60% when not summarizing images, and 90% when it does. In summary, the strategic advantages of our technology are:

Heterogeneous Multimodal Data

Generate responses from text data, as well as visual data such as images and graphics. Particularly suited for business documents (ppt, pdf, word).

Data from Multiple Sources

Respond using data distributed across multiple sources or documents.

One-Click Source Verification

The ability to view screenshots of document pages used to generate responses, with just one click.

After a preparatory phase that transforms your knowledge base of various formats (pdf, word, pptx, etc.) into an easily and quickly exploitable knowledge base, RAG operates in two steps:

Retrieval: This phase involves exploring your data to collect relevant information to answer the query. It performs a semantic comparison between the query and a text segment in our index. The text excerpts most semantically similar to the query are retrieved to serve as context for the LLM.

Generation: The second step is to generate a natural language response to the question.

Although RAG may seem simple, its impact on the enterprise is significant. Unlike traditional language models that are sometimes restricted to outdated data from several months or years ago, RAG allows the model to stay up-to-date.

“The multi-modal RAG approach improves accuracy by integrating visual elements, providing a richer context than text-only methods.”

Limitations of basic RAG

In its basic version, RAG also has limitations for enterprise use, including:

  • Loss of Global Context: Paragraphs isolated by semantic search may not contain all the necessary information to fully understand the topic or may be misinterpreted without adjacent sections.
  • Loss of Structural and Multimodal Information: Your documents may include text, but also graphics, tables, and other visual elements. Standard text encoding ignores these elements, which can be crucial for understanding the complete information.
  • Granularity Issues: The choice of text segment granularity has a significant impact on the performance of the RAG system. Paragraphs may not be of the appropriate granularity for certain types of information or questions.

Advanced RAG overcomes the limitations of simple RAG in an enterprise context: it is capable of retaining 100% of the information in its search tool during indexing, transforming it so that an LLM can better understand the information context, and answering a wider range of questions.

  • Preserving Global Context: Thanks to multi-level indexing and user query-based retrieval, the most relevant chunk (a document segment) is extracted along with the document it belongs to. This enables a deeper understanding of the context.
  • Incorporating Documents and Visual Elements: With RAG vision, visual information is preserved. This simply means integrating image processing capabilities into the RAG model.
  • Automatic Metadata Assignment: Metadata are structured pieces of information that describe, identify, and organize data. They enrich the understanding and searchability of information across different data sources (texts, pdfs, visuals, etc.).

Priority on Data Security

A fundamental aspect of our technology is our commitment to maintaining data security and compliance: Cloud Integration: Our solutions can be integrated into your existing cloud infrastructure, allowing you to maintain full control over the data, in line with your internal policies. Data Confidentiality: Our indexing ensures confidential access. Users only interact with documents they are authorized to access, preserving data confidentiality. GDPR Compliance: We adhere to European GDPR standards.
Cloud integration
Data confidentiality: users only interact with the documents they have access to
GDPR compliance
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