Turn your documents into a reliable knowledge layer your team can query in natural language.
We build production-ready RAG (Retrieval-Augmented Generation) systems that connect your PDFs, docs, wikis, CRMs, and databases to large language models — delivering accurate, cited answers instead of hallucinated guesses.
What is RAG and why does it matter?
RAG stands for Retrieval-Augmented Generation. Instead of asking an AI to answer from memory (which causes hallucinations), RAG first retrieves the most relevant chunks from your actual data, then feeds them to the LLM as context. The result: answers grounded in your real documents, with source citations.
This is the difference between a generic chatbot and a knowledge assistant your team actually trusts. Support agents get instant answers from product docs. Sales reps pull competitive intel in seconds. Legal teams query contract clauses across thousands of PDFs.
Our RAG implementation process
Data audit & ingestion
We catalog your knowledge sources — PDFs, Confluence, Notion, SharePoint, databases, CRM — and build automated ingestion pipelines.
Chunking & embedding
Documents are intelligently split into semantic chunks, embedded using state-of-the-art models, and stored in a vector database (Pinecone, Weaviate, or pgvector).
Retrieval & reranking
When a user asks a question, we retrieve the top relevant chunks using hybrid search (semantic + keyword) and rerank for precision.
LLM generation with citations
The retrieved context is fed to GPT-4, Claude, or your preferred LLM. Every answer includes source citations — no hallucination without evidence.
Production deployment & monitoring
Deployed on your infrastructure (AWS, Azure, GCP) or ours. Includes latency monitoring, answer quality tracking, and automatic reindexing.
Industries we serve
How much does RAG implementation cost?
A focused RAG system for a single use case (e.g., support copilot or internal knowledge base) typically costs $3,000–$8,000 and takes 2–4 weeks. Enterprise deployments with multiple data sources, SSO, and custom UI are scoped individually. We always start with a free 20-minute discovery call to understand your needs before quoting.
Why ZYNETRA for RAG?
- ✓We've shipped 12+ production RAG systems across industries
- ✓We built presales.ai — a live RAG + agent product used daily
- ✓We deploy on YOUR infrastructure — your data never leaves your cloud
- ✓We include monitoring, quality tracking, and automatic reindexing
- ✓We deliver in weeks, not quarters — 2–4 weeks for standard deployments
Ready to build your RAG system?
20-minute discovery call. No pitch deck. Just a direct conversation about your data and use case.