RAG Expertise

Scalable, secure retrieval systems that connect LLMs to your real-world data:

  • Vector Databases: Pinecone, Weaviate, Chroma, Qdrant, Milvus, or custom solutions
  • Semantic Retrieval: Dense and hybrid search, OpenAI or sentence-transformer embeddings
  • RAG Pipelines: LangChain, LlamaIndex, or custom frameworks with advanced context control
  • Context Engineering: Prompt injection, summarization, relevance weighting, chunk fusion
  • Latency Optimization: Caching, async batching, low-latency embedding + retrieval workflows
  • Production Systems: API endpoints, usage monitoring, logging, error handling, scale tuning

Implementation Examples

  • Hybrid RAG Search: Dense + sparse retrieval using BM25, embedding, and re-ranking
  • Conversational RAG: Multi-turn chat with memory-aware retrieval
  • Query Expansion: Synonym and keyword expansion for more intelligent recall
  • Real-Time RAG: Dynamic document updates with live indexing
  • Chunk Optimization: Structured chunking with scoring and semantic context
  • RAG on PDFs: Structured processing of PDF, DOCX, and HTML files with embeddings

RAG Development Process

  1. Document Processing Prepare and chunk documents, generate embeddings, and populate your vector DB

  2. Retrieval Pipeline Design Implement fast, relevant search with hybrid logic, filtering, and scoring

  3. Context Injection & Generation Combine top results with optimized prompts and context window strategies

  4. Deployment & Optimization API-based access, latency tuning, monitoring, and scaling strategy

Investment & Pricing

  • Basic RAG System: $20K–40K Simple vector DB, dense search, and prompt injection

  • Advanced RAG Pipeline: $40K–80K Hybrid retrieval, chunk tuning, query expansion, and context optimization

  • Production RAG Platform: $80K–150K+ End-to-end platform with monitoring, real-time updates, and scale

  • R&D & Custom Retrieval: $150–250/hr Advanced research, re-ranking models, or custom retrievers

  • Ongoing Support: Monthly support for updates, optimization, and scaling

See RAG in Action

Try a live demo of hybrid semantic search and optimized generation pipelines. See how a smart RAG system can turn static content into dynamic, searchable knowledge.

Ready to Build Your RAG System?

Let’s architect your retrieval pipeline, embed your knowledge, and build smarter AI. I help Triangle area companies turn documents into production-ready context with RAG.