How Much a Corporate RAG System Costs: Your AI Knowing Your Company

A corporate RAG system —a system that allows your AI to respond with your company's knowledge instead of internet generalities— costs between €10,000 and €90,000 to implement: €10,000 to €20,000 if the knowledge resides in an organized source, €20,000 to €50,000 if there are multiple sources and profile-based permissions, and €50,000 to €90,000 or more for thousands of documents, fine-grained access control, and continuous updates. On top of that, there is an operating cost of between €100 and €1,000/month.

From here on, in plain language: it is your company's memory put at the service of AI. It connects your documentation —contracts, manuals, offers, procedures, project history— searches for what's relevant to each question, and responds by citing the source. The usual surprise isn't in the technology, but in this: the price depends more on the state of your documentation than on the AI.

What exactly you are buying when you buy this

Four pieces: the connectors that read your knowledge sources (document manager, email, internal wiki, shared folders), the engine that chunks and indexes documents to search them by meaning rather than just exact word, the permissions layer that decides who can see what, and the interface —an internal chat, or integration with the tools where your team already works—. The quality of the result depends mostly on the first and last mile: what goes into the system and how what comes out is evaluated.

The three levels and their prices

Basic: one source, one team (€10,000-€20,000)

A reasonably organized knowledge repository —hundreds of documents, not tens of thousands—, a single access level, and a clear use case: support responding using manuals, sales consulting previous offers, operations finding the current procedure. It is implemented in 4-6 weeks and is the right entry point for most SMEs.

Medium: multiple sources and permissions (€20,000-€50,000)

Here, several systems come in at once —document manager, CRM, email, wiki— through native connectors with your systems, and the requirement that increases cost the most appears: permissions. Ensuring the AI doesn't show payroll info to someone who shouldn't see it isn't a detail, it's half of the engineering. Automatic updating also appears: when someone changes a procedure, the system must reflect it without anyone re-indexing manually.

Advanced: entire company, with fine-grained control (€50,000-€90,000+)

Thousands or tens of thousands of documents, permissions by role and by document, multiple languages, traceability of each query, and continuous response quality evaluation. This is the level for companies with hundreds of employees or in regulated sectors, where data is often required to stay within controlled infrastructure, adding deployment and operating costs.

What makes it more expensive and what makes it cheaper

Increases cost

• Chaotic documentation: duplicate versions, unmarked obsolete documents, knowledge that only exists in someone's head. Cleaning before indexing can be half of the project.
• Each additional source: every system that needs to be read adds a connector, along with its maintenance.
• Fine-grained permissions: replicating who-can-see-what from your source systems is one of the most expensive things to do well.
• Privacy requirements: personal or confidential data requiring anonymization, or the obligation that nothing leaves your servers.

Reduces cost

• Starting with one use case with a specific team, and measuring before expanding.
• Prioritizing the 20% of documents that answer 80% of actual questions.
• A canonical source: if the company already has the habit of "this lives here," the project flies.
• Leveraging existing tools: if your office suite already includes AI search that is acceptable for your case, you might not need a custom system yet.

Monthly operating cost

Three items: AI model consumption (€100 to €600/month for typical internal use, depending on query volume), search and indexing infrastructure (€50 to €400/month, more for private deployments), and content maintenance —which is the item everyone forgets—. Someone must own the quality: reviewing questions without a good answer, marking obsolete documents, incorporating new sources. Without that owner, the system loses internal credibility in six months, and a knowledge system that the team doesn't trust is money wasted.

The most expensive mistake: indexing everything on day one

The temptation is to connect all company folders and "let the AI figure it out." The result is a system that responds with obsolete documents, mixes versions, and gives different answers depending on the day —and the team stops using it—. The sequence that works is the opposite: one use case, the minimum sources that cover it, two weeks of testing with real users measuring what percentage of questions get a useful response, and expanding in phases with that metric in hand.

The figure that matters: search hours and wrong answers

The return has two components. The visible one: search time. If 30 employees lose 30 minutes a day searching for information or asking a colleague, that's about 330 hours a month; at €25/hour, over €8,000/month. A €30,000 system that cuts that in half pays for itself in less than a year. The invisible one: decisions made with the wrong version of a document, offers made without knowing a similar one already existed, knowledge that leaves with every person who churns.

Furthermore, this system is the foundation upon which greater things are later built: an autonomous AI agent that acts on your processes needs exactly this knowledge to make good decisions, and the ranges for that next step are detailed in how much a custom AI agent costs. If you want to know what level your company needs and the state of your documentation before spending, our diagnosis answers those two questions with numbers.

Frequently Asked Questions

Isn't it enough to just use ChatGPT and upload documents?

For individual and occasional use, yes. It stops being enough when you need permissions (not everyone can see everything), self-updating sources, response traceability, and guarantees about where your data ends up. That is precisely what you buy with a corporate system.

How long does it take to implement such a system?

From 4 to 6 weeks for the basic level and 8 to 12 for the medium level, including testing with real users. The variable that shifts the timeline most is not technical: it is the state of your documentation and the availability of those who know where everything is.

Does my data leave the company?

It depends on the design. Usually, cloud models are used with contracts that exclude the use of your data for training, which is sufficient for most cases. If your sector requires more, private or on-premise infrastructure deployment is available, with implementation and operating surcharges that must be justified.

An internal content owner ( a few hours a week, not a new role), periodic review of questions without a good answer, and a technical budget of 15-25% annually of the initial cost for connectors, improvements, and model evolution. This is the difference between a system that improves with use and one that gets abandoned.