RAG Pipeline Architecture
Agents that answer from your data, not guesses.
The retrieval layer behind your chatbots and agents: ingestion, chunking, vector search and accuracy testing over your documents, CRM and knowledge base.
A model that does not know your business makes things up.
Out of the box, an LLM answers from general knowledge. Ask it about your policies, stock or pricing rules and it will sound confident and be wrong. Retrieval fixes that, but only when it is built and tested properly.
- Confident answers that are simply wrong
- Knowledge scattered across docs and tools
- No way to measure answer quality
Three steps from call to launch.
Map the sources
We inventory the documents, databases and tools the agent should answer from, and what it must never expose.
Build the pipeline
Ingestion, chunking, embeddings and vector search, tuned against a test set of real questions.
Measure and maintain
Accuracy is tracked over time and content is re-indexed as your sources change.
What you get.
Source inventory and access rules
Ingestion from documents, Notion, SharePoint and databases
Vector database setup and tuning
Retrieval test set built from real questions
Answer citations back to the source
Re-indexing schedule and monitoring
AI knowledge base over 10k docs as embedded widget
Fixed-scope project Β· 14-day delivery
Cybersecurity SaaS. We built a RAG-powered chat over their full doc corpus, embeddable in any customer portal.
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Book a free 30-minute call. We map your funnel and send a fixed-scope quote within 24 hours.
Often built together.
AI Agents & Voice
Sales agents, support agents, voice receptionists and appointment setters that book calls and qualify leads around the clock.
Explore βAI Chatbot Development
WhatsApp, Instagram, Messenger, web, SMS and voice bots that sell, support and qualify 24/7, built on the Static / Dynamic / Hybrid model.
Explore βSecurity Audits
An independent review of your chatbot, agent and automation stack: data handling, access control and failure modes, before or after launch.
Explore βFull-Stack & SaaS
MVP to scale on Next.js, Node and Supabase: auth, billing, dashboards and AI features, shipped in weeks, not quarters.
Explore βAnswers, before you book.
What is RAG, in one line?
Retrieval-augmented generation: the agent looks up the relevant passages in your own content first, then answers from them.
Where does my data live?
In infrastructure you control. We set up the vector store in your own cloud or account and document who can access it.
How do you know the answers are right?
We build a test set from real questions and score retrieval before launch, then keep scoring as content changes.