2-18 DX-AI Advisor: Ontology-driven Secured AI Advisor

Main Topic: The podcast explores the challenge of extracting reliable insights from large volumes of documents (PDFs, Word docs, PowerPoints, etc.) and introduces an AI-driven solution, specifically focusing on an approach using ontologies for enhanced reliability, context-awareness, and precision, particularly exemplified by a prototype called the DX AI Advisor working with Gemma 3 models.

Problem Addressed:

• Information overload ("drowning in documents").

• Difficulty in extracting specific, key insights accurately and reliably.

• Standard search methods (like basic vector search) might lack deep contextual understanding and relationship awareness.

Proposed Solution & Key Concepts:

1. AI Advisor for Documents: An AI system designed to act as a personal assistant or expert, sifting through documents to provide specific, needed information (e.g., competitor analysis, market trends, industry shifts).

2. Ontology-Driven Approach: This is the core innovation discussed.

   • What it is: An ontology is described as a structured "map of knowledge" or "knowledge graph."

   • How it's built: The AI (like the DX AI Advisor) parses documents, uses a Large Language Model (LLM like Gemma 3) to identify key entities (concepts, companies, people, etc.) and the relationships between them. This forms the ontology, often stored in a standard format (like TTL).

   • Why it's better: Unlike just looking for keyword or semantic similarity (like basic vector search), the ontology understands the context and how things are connected.

3. DX AI Advisor Prototype:

   • Takes various document types as input.

   • Parses text and uses an LLM (Gemma 3) to build the ontology and create embeddings.

   • Stores embeddings in a vector database (Faiss) for semantic search.

   • An "AI Advisor Agent" orchestrates the process, using boththe ontology and the vector database to answer questions and generate reports.

4. Comparison of Search Methods:

   • Vector Search: Finds semantically similar text chunks (good baseline).

   • Ontology Search: Allows precise queries based on relationships (e.g., "competitors of X," "products related to trend Y"), filtering by entity types and connections.

5. Reliability Advantages of Ontologies:

   • Contextual Awareness: Understands meaning within context.

   • Reasoning: Can infer implicit connections.

   • Transparency: Allows tracing answers back to evidence via the structured ontology.

   • Consistency: Provides a consistent knowledge representation.

   • Scalability: Can grow as more documents are added.

Specific Use Cases & Features:

• Secure Environments: The DX AI Advisor is designed to work with smaller, efficient models like Gemma 3 and can run locally (using tools like Ollama), keeping sensitive data secure.

• Precise Q&A: Answers highly specific questions about relationships within the documents.

• Trend Analysis Reports: Generates structured, evidence-based reports automatically, flowing logically based on the ontology's relationships, not just listing points.

Conclusion: The ontology-driven approach offers a significant step forward in creating reliable, context-aware AI tools for document analysis, especially valuable for researchers, analysts, and R&D professionals, particularly in secure or resource-constrained environments. It moves beyond simple information retrieval to deeper understanding and reasoning. The podcast concludes by asking listeners to consider other fields where this ontology-based approach could be beneficial.