Your Company’s Knowledge Is Trapped
Knowledge workers spend 1.8 hours per day searching for information. Not doing their job. Searching for what they need to do it.
For a team of 20, that’s four people’s worth of salary burned on “where’s that document?” Every single week.
Only 27% of companies have proper enterprise search tools. The rest rely on shared drives, Slack threads, and “ask the person who’s been here longest.” That approach works until that person goes on vacation. Or quits.
What RAG Actually Is
RAG stands for retrieval-augmented generation. Sounds academic. The concept is simple.
You take an AI language model (GPT-4, Claude, an open-source alternative) and connect it to your internal documents. SOPs, wikis, project files, email archives, Slack history, CRM notes.
When someone asks a question, the system searches your documents first, finds the relevant passages, then feeds those passages to the AI model along with the question. The model generates an answer grounded in your actual data.
Not hallucinated facts. Not generic internet knowledge. Your company’s specific information, cited with sources.
Why Not Just Use ChatGPT?
Generic AI models know nothing about your business. Ask ChatGPT about your refund policy and you’ll get a made-up answer that sounds confident.
RAG fixes this by grounding the model’s responses in your real documents. The model can only reference what’s actually in your knowledge base.
That grounding is what makes it useful for business. When an employee asks “What’s our SLA for enterprise clients?” the answer comes from your actual contract templates, not from the model’s training data.
How It Works (Architecture)
A RAG system has three core components. Each one is a design decision that affects quality and cost.
The document pipeline ingests your files, splits them into chunks, and converts each chunk into a vector embedding (a numerical representation that captures meaning). These embeddings get stored in a vector database.
The retrieval layer takes a user’s question, converts it to an embedding, and finds the most similar document chunks. This is semantic search: matching by meaning, not just keywords.
The generation layer feeds the retrieved chunks plus the question to an LLM. The model synthesizes an answer from the provided context and cites its sources.
The whole round-trip takes 2-5 seconds for most queries. Fast enough for real-time use.
What It’s Good For
Internal knowledge search is the obvious use case. “How did we handle authentication on Project X?” “Where’s the Q3 report for the Munich account?” “What’s our policy on remote work in Portugal?”
No more digging through nested folder structures. No more pinging colleagues.
Customer support benefits enormously. Connect RAG to your help docs and product knowledge base. Support agents get instant answers with citations instead of searching through 50 help articles manually.
LinkedIn’s customer service team integrated RAG with knowledge graphs and achieved a 77.6% improvement in retrieval accuracy with 28.6% faster resolution times. Substantial numbers.
Onboarding is where the impact compounds. New hires who’d normally spend weeks building a mental map of “who knows what” can get productive in days.
We’ve deployed RAG for teams from 15 to 200 people. The reaction in the first week is always the same: genuine surprise that it actually works.
The Data Quality Problem
RAG systems have a hard truth: garbage documentation in, garbage answers out. The AI can only retrieve what exists.
If your SOPs haven’t been updated since 2019, the system will confidently serve outdated information. If your wiki is a graveyard of half-finished pages, retrieval quality suffers.
This isn’t a reason to delay. It’s a reason to start. Most companies don’t realize how bad their documentation is until they try to make it searchable.
The cleanup process itself has value. You’ll discover duplicate procedures, contradictory policies, and critical knowledge that exists only in one person’s head.
Building vs. Buying
Off-the-shelf RAG products exist. Glean, Guru, Notion AI, and others offer plug-and-play knowledge search. They work for generic use cases with standard document types.
Custom RAG makes sense when your documents are specialized, your security requirements are strict, or you need deep integration with existing systems.
Healthcare companies with HIPAA requirements, law firms with confidentiality obligations, manufacturers with proprietary technical documentation. These organizations need control that off-the-shelf tools can’t provide.
The build cost for a custom RAG system ranges from EUR 25,000-60,000 depending on data volume and integration complexity. Off-the-shelf tools run EUR 10-30 per user per month.
For companies under 50 people, off-the-shelf usually wins on cost. Above that, or with complex data, custom starts making sense.
Common Pitfalls
Chunking strategy matters more than model selection. How you split documents into searchable pieces determines retrieval quality.
Too small and you lose context. Too large and you dilute relevance.
Most systems use 500-1,000 token chunks with 100-200 token overlap, but the optimal size depends on your document types.
Ignoring metadata is the second mistake. A chunk that says “revenue increased 15%” is useless without knowing which quarter and which product line. Attach metadata to every chunk: document title, date, author, section heading.
Not measuring retrieval quality is the third. Track how often users find what they need on the first query. If that number drops below 70%, your retrieval layer needs tuning.
What It Costs to Run
Vector databases (Pinecone, Weaviate, Qdrant) run EUR 50-500/month depending on data volume. LLM API costs depend on query volume: roughly EUR 0.01-0.05 per query for GPT-4 class models.
A team of 50 people making 20 queries per day generates about 1,000 queries daily. At EUR 0.03 per query, that’s EUR 30/day or roughly EUR 900/month.
Add hosting, monitoring, and maintenance: total ongoing cost for a mid-sized deployment runs EUR 1,500-3,000/month. Compare that to the cost of 1.8 hours per day per employee spent searching.
The enterprise search market hit $6.83 billion in 2025, projected to reach $11.15 billion by 2030. Companies are investing because the ROI is clear.
For a broader perspective on AI use cases with proven ROI, check our overview of practical AI applications. And if you’re evaluating which LLM to use for your RAG system, our LLM comparison guide breaks down the trade-offs.
Want to make your company’s knowledge actually searchable? Let’s scope a RAG system for your team. We’ll assess your data, estimate costs, and tell you honestly whether build or buy makes more sense.
