RAG Pipeline Development
I connect your business data to AI tools so your chatbots, content, and internal systems give answers grounded in your actual products and policies, not generic guesses.
What's Included
The components of a retrieval-augmented AI pipeline built around your data.
Data Ingestion Pipeline
Your product catalog, documentation, policies, and FAQs are structured into a knowledge base the AI can search.
Vector Database Setup
Your content is converted into embeddings and stored in a vector database for fast, relevant retrieval.
Retrieval-Augmented Chat
Chatbots and assistants that look up your real data before responding, instead of relying on generic training data.
Content Grounding for AI Writing
AI content tools reference your actual product details and policies, reducing inaccurate or generic output.
Ongoing Sync
The knowledge base updates automatically as your products, pricing, or policies change, so answers stay current.
How It Works
Map Your Data Sources
We identify which documents, catalogs, and systems should feed the AI's knowledge base.
Build the Pipeline
Data is ingested, chunked, and embedded into a vector database ready for retrieval.
Connect to Chat or Content Tools
Your chatbot or content generation tools are connected to the retrieval pipeline.
Test and Sync
We test accuracy against real questions and set up ongoing sync so the knowledge base stays current.
Frequently Asked Questions
RAG, retrieval-augmented generation, means an AI model looks up your real data before answering instead of relying only on what it learned during training. For marketing, this means chatbots and content tools give accurate, on-brand answers instead of generic or outdated ones.
Common sources include product catalogs, pricing sheets, help center articles, policy documents, and past marketing content. Most structured and semi-structured business data can be connected.
Not necessarily. In many cases the existing chatbot interface stays the same, but the responses it generates are connected to a retrieval pipeline so answers are grounded in your real data.
Before launch, I test the pipeline against a set of real questions and compare the answers to your source documents. After launch, the knowledge base is kept in sync as your data changes.
Ready to Ground Your AI in Real Data?
Book a free 20-minute call. We will look at what data you have and how a RAG pipeline could connect it to your AI tools.
Book My Free Strategy Call