RAG (Retrieval Augmented Generation)

Build End-to-End RAG Systems — Without the Complexity

Create fully functional Retrieval-Augmented Generation (RAG) pipelines without manually stitching together disconnected tools. A powerful visual workflow builder combined with 500+ integrations lets you manage every stage of the RAG lifecycle in one unified system.

From data ingestion to intelligent retrieval, everything works together seamlessly.

Ingest & Prepare Data at Scale

Connect to virtually any data source—internal or external—and turn raw information into AI-ready knowledge.

• Ingest data from databases, cloud storage, APIs, CRMs, and documents
• Clean, normalize, and transform data automatically
• Chunk content intelligently for optimal retrieval performance
• Generate and store vector embeddings using your preferred models

No glue code. No fragile pipelines.

Accurate Retrieval, Grounded Responses

Ensure AI outputs are context-aware, relevant, and trustworthy.

• Query vector databases and structured data sources in real time
• Retrieve only the most relevant context for each prompt
• Inject retrieved knowledge directly into LLM requests
• Maintain short-term and long-term memory across conversations

Your AI responds based on facts—not hallucinations.

Reduce Hallucinations with Real Data

Minimize the risk of irrelevant or inaccurate AI responses by grounding LLMs in your proprietary knowledge.

• Connect models to internal documents, policies, tickets, and records
• Keep sensitive data within your infrastructure
• Update knowledge sources continuously without retraining models
• Apply deterministic logic alongside AI reasoning

The result: reliable, explainable, production-grade AI behavior.

Control, Observability & Confidence

RAG systems don’t just need intelligence—they need governance.

• Human-in-the-loop validation for critical outputs
• Pre-defined logic to control when and how retrieval happens
• Full visibility into retrieved documents and model inputs
• Built-in error handling and fallback strategies

Know exactly why an AI response was generated.

From Prototype to Production — Faster

Whether you’re building internal assistants, support bots, search experiences, or knowledge copilots, you can go live faster without sacrificing reliability.

• No custom orchestration layer required
• Easy to iterate, test, and scale
• Works with any LLM, vector store, or data source
• Designed for real-world workloads

One RAG Platform. Real Business Impact.

Stop managing fragile pipelines and start shipping grounded, scalable AI systems that your teams can trust.

RAG done right—end to end, in one place.