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Artificial Intelligence & Generative AI

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation connects large language models to your own knowledge so answers are accurate, current, and citable — not hallucinated. We design the full pipeline: ingestion, chunking, embeddings, vector search, reranking, and generation, tuned and evaluated for your domain and deployed securely on-prem or in the cloud.

Capabilities

  • Document ingestion & smart chunking
  • Embeddings & vector database setup
  • Hybrid search & reranking
  • Cited, grounded LLM responses
  • Retrieval evaluation & tuning

What you get

  • End-to-end RAG pipeline
  • Vector store & retrieval API
  • Evaluation harness & accuracy benchmarks
  • Secure on-prem or cloud deployment
Where it delivers

Common use cases

Enterprise knowledge search

Policy, legal & compliance Q&A

Support & product documentation assistants

Part of Artificial Intelligence & Generative AI

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