Graph Analytics

Problem Statement:

Traditional systems lack transparent audit trails for LLM-user interactions, making it difficult to:

• Trace data provenance — Identify which data sources contributed to a specific response.
• Reconstruct query paths — Understand how the LLM reasoned through the data.
• Verify compliance — Demonstrate alignment with policies and regulations during audits.

Solution:

A graph database can create a traceable audit trail by:

• Modeling data sources and user interactions as nodes and edges.
• Logging interactions, linking queries to retrieved data and outputs.
• Tracking query paths and dependencies, preserving reasoning chains.
• Maintaining data versions and snapshots for immutable records.

Value Add:

• Full Traceability: Retrace steps to show exact sources and reasoning paths.
• Provenance Assurance: Guarantees data lineage and accountability.
• Error Investigation: Identifies issues by linking outputs to specific data.
• Compliance Readiness: Provides queryable audit logs to meet regulations.
• Transparency: Demonstrates input-output influence to reduce bias and errors.

Problem Statement:

Pure vector-based Retrieval-Augmented Generation (RAG) systems rely solely on semantic similarity to retrieve relevant documents based on embeddings. While effective for keyword and contextual matching, they often struggle with:

• Complex relationships between entities (e.g., hierarchies, dependencies).
• Multi-hop reasoning (linking data points across disconnected documents).
• Contextual gaps caused by unstructured or loosely connected data.

Solution:

Graph-Augmented RAG integrates a knowledge graph into the retrieval pipeline, enriching the semantic search with explicit relationships and structured metadata extracted from documents. The process includes:

• Graph Construction: Entities and relationships are mapped into a Neo4j graph database or similar graph structure.
• Hybrid Querying: Combines vector similarity with graph traversal queries to leverage both contextual embeddings and logical relationships.
• Enhanced Reasoning: Enables multi-hop traversal to connect distant but related information (e.g., finding dependencies between contracts or policies).

Value Add:

• Contextual Accuracy: Enhances retrieval by combining semantic relevance (vectors) with logical connections (graphs).
• Multi-Hop Reasoning: Supports complex queries requiring traversal through relationships —e.g., linking a vendor, its contracts, and associated obligations.
• Reduced Ambiguity: Resolves context gaps and overlaps by integrating explicit relationships between data points.
• Improved Scalability: Efficiently scales as new nodes and relationships are added, maintaining data integrity and search performance.
• Insight Generation: Uncovers hidden patterns and dependencies critical for compliance, governance, and risk analysis.

Problem Statement:

The connections that define the day-to-day work of modern organizations are complex and dynamic. However, understanding them is key to be able to answer questions like:

• Who are the people/teams holding it all together?
• Who can we not afford to lose?
• Who seems to be overburdened?
• Who is becoming detached from their colleagues?
• Who is communicating across Chinese walls?

Solution:

Developing a graph-based approach to map who is e-mailing, chatting, writing or co-signing documentation, and is in meetings with whom provides a network where graph analysis can be applied. Steps to be taken involve:

• Modeling employees and interactions as nodes and edges; storing details as node and edge properties.
• Ingesting company-hierarchy data
• Running graph algorithms such a different centrality metrics to identify key/lynchpin resources
• Running clustering algorithms to identify groupings of closely-connected employees

Value Add:

Being able to quickly identify the people who are engaged in most things at once, the people that span the broadest across the organization in their daily work, the ones who act as brigdes between network-identified groups (e.g. project teams). Each of these insights can be used proactively to

• find people who at risk of burning out/coming down with stress
• avoid creating organizational gaps when reorganizing - (re)moving that key connection point may slow things down significantly for those relying on information being passed through or generated by that person
• optimize seating planning to put highly integrated teams physically close to each other
• identify suspicious communication patterns