Hire RAG/Graph RAG Developers

Traditional RAG retrieves documents from a vector store and passes them to an LLM. Graph RAG adds a knowledge graph layer to model relationships and context between entities, improving accuracy and reasoning in complex use cases.

Yes. TFT’s developers can integrate RAG systems with SharePoint, Confluence, Notion, databases, or custom CMSs to extract relevant knowledge, index it, and use it in real-time responses.

Commonly used tools include LangChain, LlamaIndex, Neo4j, TypeDB, Weaviate, Pinecone, FAISS, and OpenAI or open-source LLMs like Mistral, LLaMA, or Falcon. Our team chooses the stack based on scalability and security needs.

Absolutely. TFT can build privacy-first Graph RAG systems that comply with HIPAA, GDPR, or SOC 2 standards, using local or private cloud infrastructure with full audit trails.

We implement automated syncing pipelines that regularly update the vector store or graph database with new documents, version control, and embeddings to keep LLM outputs accurate and relevant.

RAG and Graph RAG excel in AI search engines, legal research tools, customer support agents, internal knowledge assistants, RFP generators, and technical documentation tools where context and accuracy are crucial.