Citation and confidence
Responses link to source passages. Low-confidence answers escalate to human review.
LLMs & RAG
Ground large language models in your enterprise knowledge with retrieval-augmented generation.
LLM integration leadership and auditors can trust
Radiantze designs RAG pipelines for policy assistants, IT knowledge bases, contract support, and customer help that must reflect current product and regulatory guidance.
Answers cite approved sources, retrieval respects document permissions, and low-confidence cases escalate to human review before they reach customers or employees.
Documents & data sources
Policies, manuals, tickets, contracts
Chunk & embed
Split text, generate vector embeddings
Vector database
Store embeddings for semantic search
Retrieve context
Find relevant passages for the query
LLM generation
Produce grounded answer with citations
Retrieval-Augmented Generation grounds LLM responses in your approved knowledge, reducing hallucination risk.
AWS
Amazon Nova
Bedrock integrationAWS-native appsMultimodal
Anthropic
Claude Fable / Sonnet
Long documentsAgentic workflowsSafe enterprise chat
Cohere
Cohere Command
EmbeddingsRerankingEnterprise search
Gemini
MultimodalWorkspace integrationSearch grounding
OpenAI
GPT-5.6
CopilotsCode assistComplex reasoning
xAI
Grok
Real-time reasoningTool useEnterprise assistants
Meta
Llama
Self-hostedFine-tuningCost control
Mistral AI
Mistral
EU residencyEfficient inferenceOpen weights
Microsoft
Phi
Efficient inferenceEdge deploymentTask-specific workloads
Radiantze capabilities
Access control
Retrieval respects document permissions so answers stay within authorized content.
Feedback loops
Correction workflows improve the knowledge base without bypassing policy approval.