I will build an enterprise rag knowledge search platform with hybrid retrieval

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Wanraj
Sommige informatie wordt in het Engels weergegeven.

Over deze dienst

Need your team to find answers across thousands of internal documents, and trust them? I build enterprise RAG (retrieval-augmented generation) search platforms that answer only from your sources, cite every claim, and say "not in the sources" instead of guessing.


What you get:

  • Hybrid retrieval: keyword (BM25), exact-identifier and vector search, fused with Reciprocal Rank Fusion
  • Cross-encoder reranking for sharper top results
  • Grounded answers with chunk-level citations
  • Multi-tenant isolation and document-level access control enforced inside every query
  • Ingestion for PDF, DOCX, HTML, scans and tables, with chunking, dedup and versioning
  • An evaluation harness with a golden question set, so quality is measured, not guessed
  • FastAPI, React/TypeScript, OpenSearch, PostgreSQL, Docker

Fully self-hostable: run it as SaaS or inside your own VPC.


Please message me before ordering with your document types, volume and preferred LLM (OpenAI, Claude, AWS Bedrock or open-source) so I can confirm scope.

Maak kennis met Wanraj

Wanraj

RAG and AI Agent, LangChain, AWS Production grade System

  • Afkomstig uitIndia
  • Lid sindssep 2025
  • Gem. reactietijd1 uur
  • Talen

    Engels, Marathi, Hindi
With 10+ years in IT and 5+ years focused on AI-driven applications and AWS cloud engineering, I build production-grade RAG systems, LLM-powered agents, AI automation workflows, and serverless AWS architectures that handle real users and real workloads—not just demos or proof-of-concepts. I spent 3 years as an AWS engineer inside AWS itself before going independent—now I build AI and cloud systems that startups and enterprises actually put into production.

Mijn portfolio

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