Verbis Graph - Enterprise Knowledge Retrieval and Reasoning Platform

The key innovation of Verbis Graph (verbisgraph.com) lies in combining GraphRAG, citation-grounded generation, and an ontology-aware semantic layer into one practical system for enterprise and regulated knowledge environments.

Most conventional approaches rely on keyword search, vector search, or standard RAG. These methods are useful for finding similar passages, but they often fail when users need answers that depend on relationships between entities across multiple documents. They also struggle with ambiguity, inconsistent terminology, and explainability.

Verbis Graph advances beyond this by:

extracting entities, relations, and source links from uploaded documents;

building a graph-based retrieval layer for multi-hop reasoning;

grounding responses in specific source passages;

adding an ontology layer that defines semantic classes and relationships.

For example, in a compliance workflow, standard RAG may retrieve several relevant policy passages but miss how a regulation, obligation, control, and exception connect across files. Verbis Graph can represent these as linked concepts and reason across them more coherently.

This gives Verbis Graph disruptive potential in sectors where generic document chat is insufficient. Its uniqueness is not only in retrieving information, but in structuring and interpreting knowledge. It moves beyond flat retrieval toward domain-aware, explainable reasoning, which is a significant step beyond the current state of practice in enterprise AI assistants.

The proposed solution is technically feasible because it builds on an already defined backend architecture and extends it with a clear next step: an ontology-aware GraphRAG layer.

The technical methodology consists of:

document ingestion and parsing from unstructured enterprise sources;

chunking, embedding, and metadata generation for semantic retrieval;

entity and relationship extraction to build a graph-based knowledge layer;

ontology mapping, which assigns semantic classes and controlled relationship types;

hybrid retrieval and grounded answer generation, combining graph traversal, semantic search, and source citations.

This approach is feasible because the core building blocks already exist in the platform: document processing, graph construction, retrieval, and answer generation. The ontology layer strengthens this architecture rather than replacing it, making implementation incremental and realistic.

The expected outcomes are:

more precise retrieval in complex document collections;

improved consistency in answers across domains with specialized terminology;

stronger explainability through source citations and semantic reasoning paths;

higher productivity for users who currently spend time manually connecting facts across documents;

a scalable knowledge infrastructure suitable for regulated sectors and collaborative enterprise environments.

Overall, the project addresses the challenge effectively by offering a technically credible path from fragmented documents to structured, trustworthy, and explainable AI-supported knowledge work.

We are collaborating with EUROHPC Cineca Leonardo for testing our solution on HPC and huge volume knowledge base stressing code for being enterprise ready.

Deployed via AWS Marketplace and Microsoft Marketplace