K-AI's Technology

K-AI's intelligence starts with its architecture

Built entirely in-house, our proprietary file parser and neural semantic graph work together to extract, structure, and restitute enterprise knowledge with unmatched precision—avoiding the limitations of chunk-based RAG systems like hallucinations, context loss, and token constraints.

This is how we ensure GenAI tools perform with context, consistency, and confidence.

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File Parser

A file parser is a technology that reads, interprets, and breaks down documents into structured data, making them understandable and usable for advanced processing.

At the heart of K-AI's capabilities lies its proprietary file parser – built in-house to deeply understand and structure unstructured data across a wide range of document formats and repositories.

K-AI's file parser extracts document content as a human being would and possesses the ability to read images, graphs, curves, diagrams, tables, etc. in Excel, PDF, PPT, or Word documents.

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Neural Semantic Graph

The core of K-AI's technology is a neural semantic graph, in the form of a neural network where each neuron represents a concept drawn from the documents, and the neurons are contextually linked by a definition.

When the K-AI solution is deployed and connected to a knowledge base, a neural semantic graph is automatically built while indexing all documents. Each document is converted to a sub semantic graph and is merged with the global semantic graph attached to the deployed K-AI Instance.

During a user query, K-AI extracts a sub-graph from the global neural semantic graph that represents all the necessary context and information found in the database documents.

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We are not a RAG

… but we can boost your RAG's performances !

Differences between our technology and a classic RAG:

Feature	K-AI's Neural Semantic Graph	RAG
Document Slicing	Semantic slicing - slicing based on document creation logic. ✓ Semantic meaning intact.	Basic, arbitrary chunking. ✗ Loss of meaning.
Document Analysis	Automatic detection of concepts and themes. Content vectorization. Generated meta. ✓ Keeps semantic context.	Basic vectorization. Embedding ✗ Loss of context.
Document Indexing	Creation of the semantic graph, following concepts and themes. ✓ Creation of global semantic context.	Pooling of raw vectors. ✗ No semantic links between documents.
User Search	Analysis of search via semantic graph. Retrieval of the right documents. ✓ Generation of contextualized response.	Retrieval of the chunks that are closest to the user's query. ✗ Generation of answers with chunks, possibility of increased hallucinations.
Document Cleansing	Use of semantic graph to detect contradictory documents with respect to themes and concepts.	Not Applicable
Knowledge Mining	Via prompt factory + Semantic graph	Not Applicable