It’s quite common that teams put a wrapper around a large language models to provide it with context: lots of documents, PDFs, databases (we call that a Retrieval-Augmented Generation, or RAG model). The first week usually goes great; everyone is happy & the model seems to be working well. In my experience it takes longer for the gaps to show up, as the novelty wears off & it becomes embedded within the team and organization.
For a system the business can rely on, we need to deal with data quality and access controls, then keep checking for drift.
To do that, I start with how we split the documents. It makes a real difference. If we put long PDFs into a database without much thought, the model can lose the detail it needs to answer a question, so splitting it into smaller chunks is important. Those chunks can be split based on the document structure (e.g. document sections) and they should have some overlap so we don't lose context at the edges of the sections.
Each piece of content also needs metadata. I attach the source URL, author and date to each chunk so we can show users where an answer came from – but I also tag it with relevant information which could aid the retrieval. All this information is what lets us provide citations. The business needs to be able to check an answer against its source, and I wouldn't expect people to trust it otherwise.
Access Control and Data Security in Enterprise RAG Systems
Access is where RAG projects can run into trouble with the risk team. The model may have access to a set of files, but that doesn't give every user the right to read them.
Take HR files, for example. If a junior developer asks about salaries and the system retrieves those files, we've got a problem.
We need to carry our existing permissions into the retrieval process. When a query comes in, we check what the user is allowed to see and filter the search results before they reach the model. We can't pass restricted data to the model and treat access as something to sort out afterwards.
We also log every prompt and response. If something goes wrong, that gives us a record we can work through to find out what happened. Auditability is absolutely vital when businesses are deploying RAG systems across departments and handling sensitive information.
Evaluating and Monitoring Enterprise RAG Performance
I still measure things like latency, but speed doesn't tell us whether the system found the right data. We need to check retrieval accuracy too, including whether the top three results are relevant to the question.
We can use RAGAS (an evaluation framework for Retrieval-Augmented Generation applications), or similar frameworks, to assess how faithfully the generated answer reflects the data it was given.
Nine times out of ten, when the model starts hallucinating, it's because the retrieval failed; the data just wasn't there, or the search was casting too wide a net.
We should run these checks every week. New data keeps coming in, and enterprise RAG systems can drift if we don't keep testing them. Doing all of this by hand is quite awkward, so we automate as much of the evaluation as we can.
Regular monitoring helps identify retrieval problems, changes in data quality and performance issues before they affect the people relying on the system.
When to Use Agentic RAG in Enterprise Applications
A user might ask for a summary of three different reports. A basic RAG setup can struggle with that, which is when I look at agentic RAG.
With agentic RAG, we let the system plan the steps it needs to take. It might search one database, find that it needs more information, then search another. This allows the system to handle queries that require information from multiple sources.
Honestly, I only go agentic when the basic setup clearly can't cut it, as there's just so much more that can go wrong. We also have more to monitor, and the extra work needs to be worth what the system can now do.
Enterprise AI Governance and RAG System Oversight
As these systems spread across departments, we can end up with dozens to look after. I've seen organisations lose track of which model belongs to whom, or what data is going into it.
That's why we built AI Sentri. It keeps a register of the AI systems you're running, with a named owner and a view of the risks for each one. It also scores your governance so you can see where you stand against things like the EU AI Act and ISO/IEC 42001.
AI Sentri shows the systems people are using, including ones that didn't go through the official channels. We need that view to keep control as the technology changes. It's hard to manage a system when the organisation has lost track of it or doesn't know who owns it.
Having visibility into deployed AI systems, their owners, associated risks and governance status becomes increasingly important as organisations scale their use of AI.
Enterprise RAG Deployment Checklist: Checks Before Going Live
Before putting an enterprise RAG system into production, I check that:
- Every response has a citation that can be checked.
- User permissions are checked before data is retrieved.
- There's a schedule for reviewing the model's accuracy.
- A named business owner is responsible for the output.
I'd hold back a release if any of these were missing. Yeah, it takes longer upfront, but I'd take that over spending three weeks explaining a data leak to the risk team.
Building Enterprise RAG Systems That Businesses Can Rely On
Building and scaling enterprise RAG systems requires more than connecting an LLM to an organisation's data. Businesses need to consider data quality, access controls, retrieval accuracy, ongoing evaluation and AI governance from the beginning.
As these systems become more widely adopted, having clear ownership and visibility into their risks becomes just as important as their technical performance.
The goal should be to build RAG systems that don't just work during an initial demonstration but remain reliable, secure and accountable as they become part of everyday business operations.
