Writer
Graph-based RAG engine for accurate, explainable enterprise AI assistants
Writer Knowledge Graph is a graph-based retrieval-augmented generation (RAG) system for building enterprise-grade AI assistants. It replaces traditional vector-only retrieval with a semantic knowledge graph to improve answer accuracy and reduce hallucinations. Designed for large organizations, it helps you ground generative AI in your own data across complex, dense knowledge bases.
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About
Writer Knowledge Graph is an enterprise-focused, graph-based retrieval-augmented generation (RAG) layer used to power AI applications and digital assistants. Instead of relying solely on vector embeddings, it stores company knowledge in a graph structure that preserves semantic relationships between data points. This is aimed at organizations that want more reliable, explainable answers from their internal AI tools.
The product targets enterprises in sectors like insurance, retail, and technology that need AI assistants to answer complex, domain-specific questions (e.g., policy details, product comparisons, sales enablement content) with high accuracy. It sits inside the broader Writer full-stack platform and feeds relevant context into Writer’s Palmyra LLMs, which are trained on large, curated datasets. Getting started appears to require going through Writer’s sales/demo process; this page does not detail specific setup steps, data connectors, or deployment environments.
According to Writer’s own benchmarking, Knowledge Graph outperforms several popular vector-based RAG approaches on RobustQA, a benchmark for open-domain question answering. Its design is explicitly optimized for dense, complex enterprise data, multi-hop questions, and scenarios where explainability matters. Writer also highlights techniques to reduce hallucinations and a retrieval-aware compression method that keeps context rich while controlling storage and cost.
However, this page focuses on the retrieval and grounding layer, not on orchestration or broader workflow automation. It does not describe how you manage schemas, ingest pipelines, permissions, or how the system integrates with existing tools and data sources. Security and compliance are emphasized in customer quotes, but concrete details (e.g., deployment options, data residency, access controls) are not provided here. If you need a plug-and-play personal assistant or a generic workflow automation tool, this offering is likely not a fit; it’s better suited to teams building custom, enterprise AI assistants and applications on top of Writer’s platform. Pricing is not publicly available on this page.
Chat and generation only—you do the doing
Writer Knowledge Graph is best understood as an advanced, graph-based RAG infrastructure layer for building accurate, explainable enterprise QA assistants. Its agentic aspects are mostly internal to retrieval and reasoning: it autonomously structures data into a knowledge graph, chooses graph traversal paths, and can decompose complex queries into sub-questions. However, it does not appear to take external actions, orchestrate multi-step workflows beyond retrieval and generation, maintain user-centric state across interactions, or expose detailed safety/permissioning mechanisms. Under this rubric, it functions as a sophisticated retrieval engine powering chat-like assistants rather than a fully agentic system.
Overall agenticness level: Level 0 (Score 3) – advanced RAG/QA system with some internal decision-making, but no real-world action-taking or long-horizon autonomy.
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