RAG Pipelines for Business: What They Are and Why You Need One

Your company's most valuable knowledge is trapped — in documents nobody reads, databases nobody queries, and the heads of employees who might leave tomorrow. RAG pipelines fix that.

The Problem: Your Data Is Everywhere and Nowhere

Every business with more than 20 employees has the same problem. Institutional knowledge is scattered across:

• SharePoint sites with thousands of documents nobody can find
• Google Drive folders nested 8 levels deep
• Slack channels where critical decisions were made and forgotten
• Employee brains (the most dangerous single point of failure in any organization)
• Legacy databases with no user-friendly query interface
• Email threads that contain the actual reasoning behind policies

The irony: you already have the data. You just can't get to it when you need it. Someone new joins the team and spends three weeks asking the same questions that have been answered 50 times. A sales rep can't find the case study that would close the deal. A compliance officer manually searches through policy documents for hours.

This is the problem RAG was built to solve.

What Is RAG? (Without the Jargon)

RAG — Retrieval-Augmented Generation — is an architecture pattern that gives AI models access to your specific data before generating a response.

Think of it this way: ChatGPT knows everything on the internet but nothing about your business. RAG bridges that gap.

Here's the simplified flow:

1. Ingestion — Your documents, databases, and knowledge sources are broken into chunks and converted into mathematical representations called embeddings
2. Storage — Those embeddings are stored in a vector database (we use PostgreSQL with pgvector — no exotic infrastructure needed)
3. Retrieval — When someone asks a question, the system finds the most relevant chunks from your data
4. Generation — Those chunks are fed to an LLM (like Claude or GPT-4) along with the question, and the model generates an answer grounded in your actual data
5. Citation — The response includes source references so you can verify where the answer came from

The critical difference from vanilla ChatGPT: every answer is grounded in your actual documents, and you can see the sources. No hallucinations about your own business.

What Does a RAG System Look Like in Practice?

For most businesses, the interface is simple: a chat window. It can live in a web app, in Slack, in Microsoft Teams, or as a standalone tool. Your team types questions in plain English and gets answers sourced from your data.

Notice two things: the answers are specific to the company's own data, and every answer cites its source. This isn't AI making things up — it's AI retrieving what your organization already knows.

How Is This Different From Enterprise Search?

Enterprise search tools (SharePoint search, Google Workspace search, Confluence search) find documents. You still have to open them, read them, and synthesize the answer yourself.

RAG finds the answer. The system reads the documents for you, synthesizes across multiple sources, and presents a direct response with citations. The difference is like asking a librarian to hand you a stack of books vs. asking them to just tell you what you need to know.

	Enterprise Search	RAG System
Returns	Document links	Direct answers + sources
Cross-references	No — one doc at a time	Synthesizes across docs
Natural language	Keyword-dependent	Full conversational queries
Understands context	Minimal	Follows up, disambiguates
Time to answer	Minutes (you read the docs)	Seconds

What Does It Cost?

Let's be direct about pricing, because most providers aren't.

Build Costs

• Small RAG (single data source, <10K docs): $15,000 – $25,000
• Medium RAG (multiple sources, access controls, 10K–100K docs): $25,000 – $40,000
• Complex RAG (enterprise integrations, custom agents, 100K+ docs): $40,000 – $75,000+

Ongoing Costs

• LLM API costs: $50 – $500/month for most teams (depends on volume)
• Infrastructure: $100 – $500/month (PostgreSQL + application hosting)
• Managed ops (optional): $2,000 – $8,000/month for monitoring, updates, and optimization

ROI Math

A 10-person team that saves 30 minutes per day per person on information retrieval:

• 10 people × 0.5 hours × $50/hr effective cost = $250/day
• 250 working days/year = $62,500/year in recovered productivity
• Build cost of $25,000 pays for itself in under 5 months

That's a conservative estimate. The real value is in answers people wouldn't have found at all — the institutional knowledge that would have left with a departing employee, the policy document buried in a subfolder, the contract clause nobody remembered.

What You Actually Need to Get Started

Less than you think:

1. Data access — We need to connect to wherever your documents live (file shares, Google Drive, databases, wikis)
2. A champion — One person who understands the pain points and can test the system with real questions
3. An honest conversation — Not every problem needs AI. Sometimes the answer is a better folder structure or a proper wiki. We'll tell you if that's the case.

You don't need to clean your data first. You don't need to migrate anything. You don't need an "AI strategy." You need someone who can connect the pipes.