RAG solutions: How to make AI actually use your company data

AI is powerful. But here’s the catch: it doesn’t know your company’s policies, contracts, or customer docs. So it guesses.

That’s the problem with most LLMs: they’re trained on public data, not your business documents. Which means answers can be wrong, even when they sound confident.

So how can you make AI actually work with your company data?

After building several RAG setups, here’s what we’ve seen works. This post shows how to connect your data to AI without wasting months or budget.

Start with a simple RAG

Most companies think building a RAG means integrating all their data sources at once. That’s a trap. It’s slow, costly, and often burns money before showing results.

The fastest way to get value is to start small.

At BRACAI we recommend:

1. Gather your key business documents (FAQs, policies, manuals)

2. Organize them into a vector database

3. Set up an AI agent that retrieves answers only from that source

Now when someone asks, “What’s our travel reimbursement policy?” the AI gives a grounded answer, citing the actual business document.

This isn’t just efficient. It’s high ROI. A small RAG delivers value in weeks, not years. Once trust is built, you can expand to contracts, customer docs, or support tickets.

Keep your knowledge base clean

Don’t dump raw data in. A RAG is only as good as the knowledge it retrieves.

That matters because 67% of data leaders say they don’t fully trust their own organization’s data for decision-making (Precisely, 2025). If your internal knowledge is messy, outdated, or duplicated, your RAG will just amplify the problem.

Think about your last AI answer. Did it sound smart but generic? That’s because the model didn’t know your internal docs. The fix isn’t more volume. It’s better quality.

Start with approved business documents like policies, FAQs, or product manuals. The goal isn’t “everything in one place.” It’s knowledge you can trust.

Store data in a vector database

Your AI needs to search by meaning, not just keywords. That’s what a vector database does.

Tools like Pinecone, Weaviate, or Supabase PGVector let you store chunks of text as embeddings with metadata.

That’s the RAG pipeline: document → chunk → embed → store.

It sounds technical, but in practice it’s as simple as: split up your docs, turn them into vectors, and keep them organized in a database.

Connect to a language model

Once your docs are in a vector DB, you can link them to an LLM (GPT, Claude, Gemini).

Here’s what happens next:

• User asks: “What’s our refund policy in Germany?”

• The system retrieves the relevant chunks

• The LLM generates an answer with citations

No retraining. No leaks. Just grounded answers.

Need help to build your RAG system?

At BRACAI, we help companies implement RAG solutions that deliver ROI fast.

We can:

• Organize your knowledge base

• Set up vector databases

• Connect them to LLM

• Automate pipelines so data always stays fresh.

👉 Want AI that finally understands your business documents and data? Send us an email today.