RAG Pipeline Development
We build retrieval-augmented generation systems that actually work in production. From architecture design to deployment, we bring deep expertise in RAG pipelines to turn your data into reliable AI-powered answers.
What We Build
End-to-End RAG Expertise
RAG Architecture Design
Design retrieval pipelines tailored to your data — document chunking strategies, embedding model selection, vector store architecture, and hybrid search with semantic + keyword retrieval.
Production Optimization
Optimize retrieval quality with re-ranking, query decomposition, contextual compression, and citation grounding. Reduce hallucination rates and improve answer accuracy.
Enterprise Knowledge Systems
Build multi-source RAG systems that connect to your existing data — Confluence, Notion, databases, PDFs, and APIs. Role-based access control and audit trails included.
Results
Proven Impact
95%+
Retrieval accuracy
<2s
Query response time
70%
Reduction in hallucinations
50-70%
LLM cost reduction
Technologies
Our RAG Stack
Case Studies
Real-World Results
LangChain Platform Migration
Migrated a legacy LLM platform to LangChain v1 with production RAG pipelines, achieving 5-10x throughput improvement.
Dynamic Model Switching
Built intelligent model routing that automatically selects the optimal LLM based on query complexity — reducing costs by 40%.
FAQ
Common Questions
How long does it take to build a production RAG pipeline?
A typical production RAG pipeline takes 4-8 weeks from architecture design to deployment. Simple use cases with clean data can ship in 3-4 weeks, while enterprise systems with multiple data sources, access controls, and evaluation frameworks take 8-12 weeks.
What vector database should I use for RAG?
It depends on your scale and requirements. For most startups, pgvector (PostgreSQL extension) is the best starting point — no extra infrastructure, good enough performance for millions of documents. For larger scale or specialized needs, Pinecone or Weaviate offer better performance and managed hosting. We help you choose based on your specific data volume, query patterns, and infrastructure constraints.
How do you reduce hallucinations in RAG systems?
We use a multi-layered approach: retrieval quality improvements (hybrid search, re-ranking, query decomposition), contextual grounding (citation tracking, source attribution), and output validation (factual consistency checks, confidence scoring). Our production RAG systems typically achieve 70%+ reduction in hallucination rates compared to naive implementations.
Can you integrate RAG with our existing data sources?
Yes. We build connectors for common enterprise data sources including Confluence, Notion, Google Drive, SharePoint, databases, APIs, and PDF repositories. Each connector handles incremental syncing, access control mapping, and document lifecycle management.
How much does RAG pipeline development cost?
Our RAG pipeline engagements typically range from $30K-75K depending on complexity. A focused RAG pipeline for a single data source starts around $30K. Enterprise systems with multiple sources, access controls, evaluation frameworks, and production monitoring are in the $50-75K range. This compares to $200K-400K+ for hiring and ramping an in-house team.
Ready to build your RAG pipeline?
Let's discuss your data, your use case, and how we can build a retrieval system that delivers accurate, grounded answers at production scale.