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.
Want to dive deeper?
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 KAI’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.
KAI’s file parser extracts document content as a human being would and possesses the ability to read images, graphs, curves, diagrams, tablets, etc. in Excel, PDF, PPT, or Word documents.
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.
… but we can boost your RAG’s performances !
Differences between our technology and a classic RAG:
|
|
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
|
join the success
Book a free consultation with our team to discover effective solutions to your Gen AI and knowledge management challenges.
