Graph-based RAG by Writer is an enterprise-grade retrieval-augmented generation solution that leverages graph technology to connect large language models (LLMs) with internal company data for precise, context-aware information retrieval. Unlike traditional RAG systems that rely on vector databases and often struggle with hallucinations and poor data handling, Writer’s graph-based approach models semantic relationships between data points, enabling multi-hop reasoning and accurate answers across complex, dense enterprise knowledge bases. The platform integrates seamlessly with existing systems, supports rapid deployment, and is designed to scale from pilot projects to full production. Writer Knowledge Graph, the core of this solution, delivers industry-leading accuracy (over 86% on RobustQA benchmarks), sub-second response times, and significant cost savings compared to DIY vector retrieval. It also ensures compliance with global privacy and security standards, making it suitable for handling confidential and regulated data.

Key Features

Graph-Based Retrieval-Augmented Generation Connects LLMs to enterprise data using graph structures for superior accuracy.

• Models semantic relationships between data points
• Enables multi-hop reasoning across documents

High Accuracy and Performance Outperforms vector-based RAG systems in benchmarks.

• Achieved 86.31% accuracy on RobustQA
• Maintains sub-second response times

Transparent and Explainable AI Exposes the reasoning process and data sources for each answer.

• Shows snippets, subqueries, and source documents
• Supports developer access via API

Scalable and Rapid Deployment Designed for easy integration and fast rollout.

• Minimal setup effort
• Scales from pilot to production

Security and Compliance Handles confidential data with robust privacy controls.

• Adheres to GDPR, HIPAA, SOC 2 Type II, PCI
• Flexible deployment options

Cost Efficiency Reduces operational costs compared to vector-based solutions.

• 67% lower costs than DIY vector retrieval
• Efficient data handling and updates

Benefits

Enterprise-Grade Accuracy and Reliability Delivers consistently correct answers for business-critical use cases.

• Reduces hallucinations and irrelevant results
• Supports complex, dense information structures

Improved Transparency and Trust Makes AI outputs explainable and auditable.

• Facilitates compliance and risk management
• Builds user confidence in AI decisions

Lower Total Cost of Ownership Efficient architecture reduces infrastructure and maintenance costs.

• Scales economically as data volume grows
• Minimizes manual intervention

Enhanced Security and Data Governance Protects sensitive information and meets regulatory requirements.

• Suitable for regulated industries
• Enables safe use of confidential data