We build Graph RAG systems that combine knowledge graphs with retrieval-augmented generation, helping AI applications understand and reason about complex relationships in your data.
Traditional RAG systems are useful for retrieving relevant content, but some business questions require more than document similarity. They require understanding how people, products, processes, events, records, and concepts are connected.
At WP Stories, we design Graph RAG solutions that model your domain as a knowledge graph, extract entities and relationships from your data, and enable AI systems to answer complex, multi-hop questions with stronger context.
What We Offer
Knowledge Graph Design and Construction
We design and build knowledge graphs that represent the important entities, relationships, and structures within your business domain.
This can include customers, products, documents, transactions, policies, teams, systems, processes, or any other data objects that need to be connected and understood.
Automated Entity and Relationship Extraction
We develop pipelines that extract entities and relationships from documents, databases, and other data sources.
This helps transform unstructured and semi-structured information into a connected knowledge layer that AI systems can use for retrieval and reasoning.
Multi-Hop Reasoning Engines
We build retrieval and reasoning workflows that allow AI systems to answer questions requiring multiple steps.
Instead of only retrieving one relevant document, a Graph RAG system can follow relationships across connected data to build a more complete answer.
Hybrid Retrieval Systems
We combine graph traversal with vector similarity search to improve retrieval quality.
Vector search helps find semantically relevant content, while graph traversal helps uncover relationships, dependencies, and paths between entities.
Interactive Knowledge Exploration Dashboards
We create dashboards that allow teams to explore knowledge graphs visually.
These interfaces can help users inspect entities, relationships, clusters, paths, and supporting information behind AI-generated answers.
Real-Time Graph Updates and Synchronization
We build systems that keep your knowledge graph updated as your data changes.
This can include synchronization with documents, databases, APIs, internal tools, or business systems, depending on your infrastructure.
Our Approach
At WP Stories, we start by understanding your domain, data sources, and the types of questions your AI system needs to answer.
We identify the key entities, relationships, metadata, and retrieval patterns needed to represent your knowledge accurately. Then we design a graph structure, build extraction pipelines, connect vector search where needed, and create retrieval workflows that support multi-hop reasoning.
Our approach focuses on relevance, explainability, scalability, and practical business use. We also consider how the system will be updated, monitored, queried, and integrated into your existing applications.
The result is a Graph RAG system that can support advanced use cases such as research assistants, compliance analysis, enterprise search, customer intelligence, fraud investigation, technical documentation, and complex decision-support tools.
Technologies We Use
- Python
- Neo4j
- LangChain
- spaCy
- OpenAI
- GraphQL
- React
- D3.js
Build a Graph RAG System for Complex Knowledge
If your business needs AI that can understand relationships, follow connections, and answer complex questions across your data, WP Stories can help you design and build a Graph RAG solution.
Contact WP Stories to discuss your Graph RAG requirements and choose the right technical approach for your project.
