There's a lot of internal data, so why can't AI cite any of it?

Many companies possess vast amounts of data, including personnel policies, labor guidelines, internal document forms, product specifications, project deliverables, and even employee contact information. The problem is that this data is confined within the intranet. Direct connection to external LLMs is impossible, and simultaneous training of all data is impractical from a security, accuracy, and operational perspective. At this point, Retrieval-Augmented Generation (RAG) needs to be redefined beyond a simple AI technology, but rather as a design philosophy for capitalizing on corporate data.

Market Need: "Readable AI," Not "Learned AI"

What businesses want isn't "AI that memorizes all of our data." Their real need is for "AI that references only the relevant data at the right moment, without losing context." In an internal network environment, the following demands arise simultaneously.

First, data will not be leaked externally.

Second, the basis for the answer must be clearly traceable.

Third, if the data changes, the AI's answers will also change immediately.

An approach that satisfies these three requirements simultaneously is RAG-centric design.

Common Misconception: "Gather them all together and teach them."

Many internal asset management projects fail here. They scrape together all the HR documents, manuals, PDFs, Excel spreadsheets, and emails, vectorize them, and then hope that AI will answer them. However, the results are often similar. The answers are plausible but subtly inaccurate, the reference point is unclear, and the resulting sentences are irresponsible. This isn't a problem with the technology; it's the removal of the human role from the design phase.

Key to Internal Network RAG Design #1: Data is not something to be "collected," but something to be "defined."

The starting point for RAG design is not collection, but classification and definition. Internal data should be restructured as follows:

– What data is fact?

– What data is policy?

– What data is a guideline?

– Which data is time-dependent?

Without this distinction, neither search accuracy nor answer reliability can be guaranteed. In other words, information architecture design must precede vector database development.

Key 2 in Internal Network RAG Design: The Search Unit is Not the Document, but 'Meaning'

Most corporate documents are not AI-friendly. Background, exceptions, and supplementary provisions are often intertwined within a single document. In an internal network, RAG requires chunks reorganized by business context, rather than simply splitting the document into individual pieces. For example, instead of a document titled "Annual Leave Regulations," it should be structured into units directly linked to the question, such as "Annual Leave Accrual Conditions," "Annual Leave Carryover Rules," and "Annual Leave Expiration Date."

Key 3 to Internal Network RAG Design: Human Intervention Should Be Changed, Not Reduced

A question that often arises in these projects is, "Is manual work like data annotation necessary?" The answer is, "Yes, but not in the form of annotation we know." In internal RAGs, the role of humans is not to tag answers, but to define "when this information is used, to which questions it should be referenced, and where are the boundaries beyond what AI can answer?" This is closer to knowledge design than data labeling.

Technical Challenges: Security, Permissions, and Logs

Internal network RAG has technically completely different requirements than external services.

– Search results should vary depending on access rights by department and position.

– There should be a log of which documents were cited and when.

– The response results must be reproducible from an audit perspective.

For this reason, the internal network RAG must be designed with a structure that combines authorization management, audit logs, and data version management, rather than simple API integration.

Iropke's perspective: RAG is not a function, but an 'organizational memory structure'.

Iropke doesn't view these types of projects as "technology for scraping data and slapping AI on it." It's a question of organizational design: how a company defines its own knowledge, what criteria it uses to accumulate it, and how and to whom it will be delivered. Consequently, the approach is different.

– We don't put the data in first. We define the question first.

– We don't choose a model first. We design the responsibility structure first.

- And finally, deploy the technology.

Conclusion: AI learns by reading the structure of data.

Enterprises already have ample data. The problem is that they lack a structure that allows AI to recognize it as an asset. In an internal network environment, RAG isn't a technology choice to reduce costs; it's an infrastructure that ensures corporate knowledge doesn't disappear. There's no need to wait for AI to become smarter. First, companies need to redesign their data into a format AI can read.