📚 How We Built AI Agents That Understand Your Business Using RAG (Retrieval-Augmented Generation)

Most AI models are brilliant -- but forget everything about you.

They don't know:

• Your product features
• Your support processes
• Your internal language or customer history
• Your documents, guides, tickets, or emails

We help companies fix this by implementing RAG-powered AI agents -- giving your AI access to your knowledge base, CRM, documents, dashboards, and even codebases.

In this blog, we'll break down:

• What RAG is
• When you should use it
• How we implemented it for real clients
• What tools we used (vector DBs, chunking, embeddings)
• The exact stack we used to deliver intelligent, private LLM agents

🤖 What is RAG?

RAG = Retrieval-Augmented Generation

It works like this:

User Question → [Retrieve context from your data] → [Inject into LLM prompt] → AI Answers accurately

RAG connects your private data (PDFs, Notion, Jira, support tickets, Google Docs, codebases) to LLMs like GPT-4, Claude, or Llama3 -- so the answers are:

• Accurate
• Contextual
• Secure
• Explainable

🧠 Why Our Clients Wanted RAG

💼 Real Use Case: Custom AI Support Agent

Client: Customer Support SaaS

Problem: Their chatbot gave wrong answers -- because the model didn't know their product guides, Jira issues, or changelogs.

We built a RAG-powered support agent that:

• Ingested product docs, support tickets, changelogs
• Used OpenAI's GPT-4 with custom system prompt
• Retrieved relevant context per query from Chroma vector DB
• Returned answers with source citation links
• Logged every retrieval + answer to BigQuery for traceability

🧱 Architecture Overview

                  +-------------------+
     User Query → |   AI Agent (LLM)  |
                  +---------+---------+
                            |
                            ↓
                  +--------------------+
                  | Retrieve Context   | ← From Vector DB (Chroma, Pinecone, Weaviate, etc.)
                  +--------------------+
                            ↓
                  +---------------------+
                  | Final Prompt Inject |
                  +---------------------+
                            ↓
                  +---------------------+
                  |    Final Response   |
                  +---------------------+

Sources = PDF, GDocs, Notion, Jira, Slack, API, GitHub, Zendesk

🧰 Stack We Used for RAG

🔄 How We Optimized the Pipeline

✅ Smart Chunking

• Split documents by semantic paragraphs, not lines
• Avoided token overflows by using overlapping context windows

✅ Hybrid Retrieval

• Combined keyword search (BM25) + embedding similarity
• Ensured rare but important keywords still retrieved relevant info

✅ Memory + History

• Used vector memory for session-based recall
• Thread history passed into agent context for smarter follow-up questions

🔐 Data Privacy & Security

• ✅No customer data ever goes to 3rd party without consent
• ✅Data encrypted at rest and in transit
• ✅Secrets (API keys, tokens) stored in Google Secret Manager
• ✅Retrieval logs + actions stored in BigQuery for full auditing

📈 Results from Deployment

💬 What the Client Said

> "Our support AI now answers like someone who's worked here for years."

> -- Head of Customer Experience

> "This is the first AI system we trust with live clients."

> -- VP of Product

📞 Want to Build Your Own RAG-Powered AI Agent?

We help companies:

• ✅Turn internal data into context-aware AI
• ✅Integrate with Notion, Jira, Slack, Docs, CRMs, and more
• ✅Build secure, traceable pipelines with source citations
• ✅Deploy AI to your team, app, or dashboard