RAG Pipeline Design & Build
End-to-end retrieval-augmented generation systems — engineered for accuracy, designed to scale, and built with hallucination controls from day one.
What's Included
A production-grade RAG pipeline, end to end.
Ingestion & Chunking Strategy
Document ingestion pipelines with chunking strategies tuned to your content types.
Embedding & Vector Store
Embedding model selection and vector database setup — LanceDB or Pinecone.
Retrieval Tuning
Hybrid search, re-ranking, and retrieval strategies tuned for relevance.
Hallucination Controls
Grounding checks, citation enforcement, and guardrails to keep responses factual.
LLM Integration
Production integration with Claude, Gemini, or your model of choice.
AWS Deployment
Deployed on AWS with monitoring and scaling built in from day one.
How It Works
From data sources to a tuned retrieval layer.
Discover
Map your data sources, formats, and retrieval requirements.
Design
Architect the chunking, embedding, and retrieval strategy.
Build
Stand up the ingestion pipeline, vector store, and retrieval layer.
Integrate
Connect to your LLM with hallucination controls in place.
Validate
Test against real queries and tune for relevance and accuracy.
Explore More
Other AI Services
Ready to build a RAG pipeline that holds up in production?
Get In Touch
Talk to the AI Services team.
AI Services Contact
AI Quality Audits, RAG pipelines, agentic workflows, and continuous monitoring.
ai@tvaksatech.com
Phone
+91 70260 02096
Hours
Calls: 9:00 AM – 6:00 PM | WhatsApp & Message: Anytime