Verbis Free Demo - GraphRAG Knowledge Graph & Vector Search

Powered by GraphRAG, Verbis Graph offers precise retrieval for AI applications, improving accuracy and AI reliability while reducing hallucinations across connected sources of information. Verbis Graph helps teams retrieve more accurate and trustworthy responses across enterprise documents and knowledge bases. It combines vector search, knowledge graph traversal, and relationship-aware retrieval to support document Q&A, knowledge assistants, enterprise search, AI output validation, AI auditability, and AI applications that require connected reasoning.

Designed for complex questions that require connecting information across multiple sources, Verbis Graph helps reduce hallucinations, improve AI reliability, and deliver cited, explainable answers across internal documents, policies, procedures, research files, and knowledge bases. It can also support compliance management workflows where source attribution, traceability, and audit-ready documentation are important.

Available on AWS as a free demo container, Verbis Graph is ideal for evaluation, prototyping, and proof-of-concept deployments before moving to production.

KEY FEATURES

GraphRAG retrieval: Combines knowledge graphs, vector search, and knowledge graph traversal to improve answers for complex, multi-source questions.

Hybrid vector search and graph reasoning: Retrieves information across documents, entities, and relationships to support multi-hop reasoning beyond simple similarity matching.

AI output validation and AI auditability: Helps teams evaluate cited, explainable responses linked to source content, supporting audit-ready documentation and trustworthy AI workflows.

Framework integrations: Works with LangChain, LlamaIndex, AutoGen, CrewAI, and Amazon Bedrock Agents for integration into modern AI applications and RAG pipelines.

Evaluation-ready on AWS: Free demo container for testing, prototyping, and proof-of-concept deployments.

WHY GRAPHRAG?

Traditional RAG systems rely mainly on vector embeddings, which can miss important relationships between concepts, entities, and events. For example, when asking "Which marketing campaigns were affected by the supply chain disruption in Q3?", vector search may retrieve similar documents without connecting the underlying facts.

GraphRAG adds knowledge graph traversal and relationship-aware retrieval, helping teams generate more complete, explainable, and reliable answers across connected sources. This makes Verbis Graph useful for AI applications that require source attribution, multi-hop reasoning, AI output validation, compliance management, AI auditability, and audit-ready documentation.

By making retrieved context easier to trace, validate, and audit across enterprise documents and knowledge bases, Verbis Graph also supports AI accuracy, AI explainability, and AI governance.

USE CASES

AI applications and knowledge assistants: Build intelligent Q&A systems over enterprise documentation, policies, procedures, and internal knowledge bases.

Enterprise search: Improve retrieval across proprietary, cloud-hosted, or locally hosted documents and knowledge bases using vector search and knowledge graph traversal.

Customer support automation: Deploy chatbots and AI assistants that provide cited answers from internal knowledge bases.

Research and analysis: Query complex information with multi-step reasoning across connected sources.

AI governance, compliance management, and legal workflows: Support AI compliance, AI explainability, AI auditability, audit-ready documentation, and cited answers for regulated environments.

AI output validation: Help teams evaluate whether answers are grounded in source content before using them in business workflows.

GETTING STARTED

Start with the free demo for evaluation and proof-of-concept projects. Verbis Graph offers self-service deployment with Python and JavaScript SDKs. Upload your documents, build your knowledge layer, and query the system through REST APIs or your preferred AI framework. Contact us for enterprise deployments and custom requirements.

FREE TIER

Includes predefined limits on request volume, data size, and throughput to support evaluation, prototyping, and proof-of-concept workloads.

INTEGRATIONS

Amazon Bedrock, LangChain, LlamaIndex, AutoGen, CrewAI, OpenAI, Anthropic Claude, Amazon Neptune.

Built by Prodigy AI Solutions. Enterprise support and custom deployments available.

Evaluate GraphRAG and vector search at no software cost. This free edition is designed for demonstrations, learning, and small document collections. For production workloads, large document sets, advanced governance, and commercial support, use Verbis Graph PRO.

Explore AI output validation through source citations, connected evidence, and knowledge graph traversal. Verbis helps users inspect how an answer was produced instead of relying only on generated text.