Services / RAG Implementation
RAG Implementation That Actually Works in Production
Most RAG demos retrieve the wrong documents. Production RAG needs hybrid search, proper chunking, multi-tenant isolation, and evaluation harnesses. CodeWheel builds RAG systems that answer questions accurately and scale with your data.
Typical delivery
4-8 weeks from kickoff to production deployment, depending on document volume and integrations.
What you get
- ✓ Document ingestion pipeline with semantic chunking
- ✓ Hybrid search with vector + keyword scoring
- ✓ Multi-tenant isolation (RLS on vector tables)
- ✓ Evaluation harness with golden question tests
- ✓ Observability dashboards for retrieval quality
- ✓ Production deployment with CI/CD
RAG Architecture
Production RAG, Not Demo RAG
The difference between a RAG demo and production RAG is retrieval accuracy, proper chunking, and systems that don't break when your data grows.
What gets built
End-to-End RAG Pipeline
Document Ingestion Pipeline
Automated extraction from PDFs, Word docs, web pages, and APIs. Semantic chunking with overlap, heading detection, and table handling.
Embedding & Vector Storage
OpenAI text-embedding-3-large or open-source alternatives. pgvector schemas with HNSW indexes, versioning for re-embeds, and backup strategies.
Retrieval Layer
Hybrid scoring combining vector similarity + keyword relevance. Configurable weights, metadata filters, and reranking with Cohere or cross-encoders.
LLM Orchestration
Context window management, prompt templates, streaming responses, citation extraction, and fallback handling for rate limits.
Testing & Evaluation
Golden question datasets, automated retrieval accuracy checks, A/B testing infrastructure, and regression detection.
Use Cases
Where RAG Delivers Value
Customer Support AI
Answer questions from knowledge bases, tickets, and documentation. Reduce support volume with accurate, cited responses.
Internal Knowledge Search
Search across Confluence, Notion, Google Drive, and Slack. Find answers without knowing where to look.
Legal & Compliance
Search contracts, policies, and regulatory documents. Extract clauses, compare versions, summarize changes.
Product Documentation
AI-powered docs that answer user questions. Reduce friction, improve onboarding, track what users struggle with.
Technology
The RAG Stack
FAQ
RAG Implementation Questions
What makes a RAG system production-ready?
Production RAG needs accurate retrieval (not just semantic similarity), proper chunking for your content type, multi-tenant isolation if serving multiple customers, evaluation harnesses to catch regressions, and observability to debug issues. Most demos skip all of this.
How long does RAG implementation take?
Typical builds run 4-8 weeks. Weeks 1-2 cover ingestion and chunking strategy. Weeks 3-4 focus on retrieval tuning and evaluation. Weeks 5-6 add LLM orchestration and production hardening. Larger document sets or complex integrations extend the timeline.
Do you work with existing vector databases?
Yes. I work with pgvector (Supabase, Neon), Pinecone, Weaviate, Qdrant, and Chroma. If you have an existing setup, I can audit and improve it rather than rebuild from scratch.
How do you handle multi-tenant RAG?
Row-level security on vector tables ensures tenants only see their own documents. Embeddings are scoped by tenant ID, and queries are automatically filtered. This is table stakes for B2B SaaS.
What about RAG evaluation and testing?
Every RAG system ships with golden question datasets, automated retrieval accuracy checks, and regression detection. You can measure precision, recall, and answer quality before and after changes.
Ready to Build Production RAG?
Let's discuss your documents, use case, and timeline. We'll share what a realistic RAG architecture looks like for your situation.
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