RAG & Knowledge Systems
Gautam AI builds Retrieval-Augmented Generation (RAG) and enterprise knowledge systems that connect large language models with trusted data sources— delivering accurate, explainable, and context-aware AI.
What Are RAG & Knowledge Systems?
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances LLMs by retrieving information from trusted knowledge sources before generating responses.
At Gautam AI, RAG systems transform scattered enterprise data into a living intelligence layer that supports decisions, automation, and insight generation with traceability and accuracy.
RAG & Knowledge System Capabilities
Knowledge Ingestion
Documents, databases, APIs, and data lakes.
Semantic Retrieval
Vector search and contextual matching.
Vector Databases
Scalable embeddings and indexing.
LLM Integration
Context-aware and grounded responses.
Secure Knowledge Access
Role-based and permission-aware retrieval.
Explainable Outputs
Source-linked and verifiable answers.
Gautam AI RAG Architecture
- Data ingestion and document processing pipelines
- Embedding generation and vector indexing
- Hybrid retrieval (semantic + keyword)
- Context injection into LLM prompts
- Security, access control, and auditability
- Monitoring, evaluation, and continuous updates
RAG & Knowledge System Use Cases
- Enterprise knowledge assistants and copilots
- Internal documentation and policy intelligence
- Customer support and helpdesk automation
- Legal, compliance, and regulatory analysis
- Research and analytics platforms
- AI search across proprietary data
Why Gautam AI for RAG Systems?
- Deep expertise in LLM + knowledge architectures
- Security-first and enterprise-ready design
- Reduced hallucinations and higher trust
- Scalable systems for large knowledge bases
- End-to-end ownership and long-term support
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