RAG & Document Intelligence: How AI Understands Your Documents

The Central Question: Can AI Understand Our Own Documents?

“Can we have AI answer questions from our own documents?” This question arises in virtually every enterprise. The answer is yes — with RAG (Retrieval Augmented Generation). The principle: your documents stay in your infrastructure. The language model is not trained on your data. Instead, relevant document sections are provided to the model as context with each query. The model answers based on those sections — with source citations.

No data egress to third parties. No retraining. No loss of control. And yet answers in natural language that are grounded directly in your corporate knowledge.

RAG is now the standard approach for connecting language models with organization-specific knowledge. This article explains how RAG works, when it is the right choice, and why Document Intelligence goes far beyond a better search function — including PII anonymization, contract redaction, and signature detection.

How RAG Works

RAG consists of two phases: indexing your documents and answering queries. The flow can be illustrated in a diagram:

Documents → Chunking → Embedding → Vector Database
                                         │
User Query → Query Embedding → Similarity Search
                                         │
                              Relevant Sections + Query → LLM → Answer with Source Citation

Phase 1: Indexing. Your documents — PDFs, Word files, HTML pages, scanned records — are split into meaningful sections (chunking). Each section is converted by an embedding model into a mathematical vector. These vectors are stored in a vector database. The vector represents the meaning of the section, not its wording. “Remote work policy” and “home office agreement” lie close together in vector space — even though they use different words.

Phase 2: Query. When a user asks a question, it too is converted into a vector. The vector database finds the sections that are semantically closest to the question — not by keyword search but by similarity calculation. These relevant sections are passed to the language model together with the original question. The model generates an answer based on these specific sources.

The result: an answer in natural language grounded in your documents — with references to the source passages the information came from.

The quality of indexing is decisive. Chunks that are too large dilute relevance. Chunks that are too small lose context. The chunking strategy — size, overlap, metadata enrichment — largely determines answer quality. A good RAG pipeline is not about the technology itself but about its configuration for your specific document landscape.

RAG vs. Fine-Tuning vs. Prompting

RAG is not the only way to give a language model domain knowledge. There are three fundamental approaches, differing in effort, cost, and suitability:

Approach	What Happens	When Suitable	Cost	Currency
Prompting	Context supplied directly in the prompt	Small data volumes	Low	Immediate
RAG	Relevant documents found automatically	Large knowledge bases	Medium	Re-indexing
Fine-Tuning	Model retrained	Specialized language/domain	High	Only via retraining

Prompting works when the relevant context fits within the model’s context window — typically a few dozen pages. For a single workplace agreement, that is sufficient. For a knowledge base with hundreds of documents, it is not.

RAG scales to large document collections. The vector database can hold hundreds of thousands of sections. With each query, only the relevant sections are retrieved and passed to the model. Documents can be updated at any time — a re-indexing step is all that is needed. The model does not need to be retrained.

Fine-Tuning alters the model’s weights themselves. This is appropriate when the model needs to learn entirely new domain language — such as medical terminology or a proprietary nomenclature — or when a very specific output format is required. Fine-tuning is costly, complex, and requires retraining with every update.

For 90% of enterprise use cases, RAG is the right approach. The combination of large knowledge bases, frequent updates, and the need for source citations makes RAG the standard for corporate knowledge.

Document Intelligence — More Than Search

RAG answers questions based on documents. Document Intelligence goes further: it encompasses all methods by which AI not only reads documents but understands, classifies, and processes them — including the protection of sensitive information.

The three most important application areas in the enterprise context: PII anonymization, contract redaction, and signature detection.

PII Anonymization: Roundtrip Pseudonymization

Personal data (PII — Personally Identifiable Information) must not be passed to a language model in many use cases. Salaries, real names, employee IDs, health data. GDPR and internal data protection policies set clear boundaries.

The solution is roundtrip pseudonymization. A concrete example:

Original document: “John Smith, Finance Department, salary EUR 85,000, enrolls in the flexible working hours agreement.”

After pseudonymization (input to the model): “Person_A, Department_X, Salary_Y, enrolls in the flexible working hours agreement.”

The language model processes the query with pseudonymized data. It never sees the real name, the department, or the salary.

After re-identification (output to the user): The placeholders in the result are replaced with the original data. The user sees the complete answer. The model never saw it.

This roundtrip happens automatically. For the user, the process is transparent. For the model, the data is inaccessible at all times. For the infrastructure, this means: the pseudonymization layer sits between user and model and is technically enforced, not optional.

PII anonymization is especially relevant for HR use cases where personnel files, payroll records, or performance reviews are to be processed with AI support. Without anonymization, such use cases are not GDPR-compliant in the EU.

Contract Redaction

Contract redaction goes beyond pseudonymization. While pseudonymization replaces data and restores it at the end, redaction physically removes content from the document — irrevocably for the given recipient.

The use case: different departments need different views of the same contract. The legal department sees the full contract. Procurement sees a version without liability clauses. The executive board sees a summary without operational details.

Redaction works on a rule basis. For each document category and each recipient group, rules define which sections are visible and which are redacted. The rules are configured and versioned in the Decision Layer — not applied manually.

The result: each role sees exactly the information that is relevant and authorized for them. No manual redaction. No forgotten passages. No versions accidentally forwarded in full.

Signature Detection

Enterprise contract archives frequently contain thousands of documents. The question of whether a specific contract is fully signed often requires manual page-by-page review. For hundreds of contracts, this is not practical.

Document Intelligence solves this through automated signature detection. The system checks scanned contracts for the presence of signatures at the designated locations. Missing signatures are automatically flagged. The result: an overview of all contracts in the archive that are not yet fully executed — in minutes rather than weeks.

The use case extends beyond mere detection. In combination with a RAG pipeline, the system can also answer questions like: “Which framework agreements with a term exceeding three years were renewed last quarter without a board-level signature?”

Practical Example: The Works Agreement Assistant

A concrete scenario from HR practice. An HR department at a mid-sized company manages over 100 active works council agreements (Betriebsvereinbarungen): working hours, remote work, business travel, professional development, retirement benefits, workplace reintegration management, data protection, IT usage, and more. Each agreement has amendments, annexes, and cross-references to other agreements.

When a case worker needs to answer the question “What is the remote work policy for part-time employees in production?” — today that means: find the right works council agreement, locate the relevant passage, check whether an amendment exists, cross-reference with the collective bargaining agreement, account for site-specific provisions. Result: 30 to 45 minutes of research time. In case of uncertainty, a query to the legal department. Another few days of waiting.

With a RAG-based works agreement assistant: all works council agreements are indexed — including amendments, annexes, and cross-references. The case worker asks the question in natural language. The system finds the relevant passages from the correct documents, accounts for the March 2025 amendment, references the special provision for production employees, and delivers the answer in 10 seconds. With source citation. With rule version.

This is not a theoretical scenario. It is the standard use case with which organizations set up their first RAG pipeline. The effort is manageable: provide documents, configure the chunking strategy, define access rights, test. The infrastructure — vector database, embedding model, language model, retrieval pipeline — is built once and then available for additional use cases.

Quality Assurance: Why RAG Results Are Only as Good as the Indexing

RAG is not a self-runner. The most common sources of error in practice:

Poor chunking strategy. Chunks that are too large (entire chapters) deliver too much irrelevant context. Chunks that are too small (individual paragraphs) lose coherence. The right chunk size depends on the document type — a technical specification requires different chunks than a works council agreement.

Missing metadata. Without metadata (document type, effective date, version, scope), the retrieval pipeline cannot distinguish between a current and an outdated regulation. Metadata enrichment during indexing is not optional — it is essential.

No access control. In an enterprise environment, not every user should be able to access all documents. The RAG pipeline must reflect the existing permissions structure: HR documents only for HR, financial data only for Finance, board communications only for authorized individuals.

No source verification. RAG provides source citations. But are they correct? Quality assurance — spot-checking source references, feedback mechanisms for users, regular evaluation — is necessary to detect hallucinations and improve the pipeline.

This quality assurance is part of ongoing operations, not a one-time setup. Documents change. New ones are added. Old ones become invalid. The RAG pipeline must evolve — through regular re-indexing, metadata updates, and user feedback.

Integration into the Enterprise AI Portal

RAG is not an isolated system. In a well-designed architecture, the RAG pipeline is integrated into the Enterprise AI Portal. Employees ask questions through a unified interface — the same one through which they also interact with AI agents.

The portal manages access rights: who may query which knowledge base? HR employees see the works agreement assistant. The legal department sees the contract assistant. Procurement sees the supplier policy assistant. Each user sees only what they are authorized to access.

The combination of RAG and AI agents opens up extended possibilities: an agent can not only answer a question but, based on RAG results, trigger an action — such as generating a deadline alert when a contract is about to expire, or producing a checklist when a new works council agreement takes effect.

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📘 Enterprise AI Infrastructure Blueprint 2026 – Article Series

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Want to make your corporate knowledge AI-accessible? Gosign builds RAG pipelines and Document Intelligence solutions for enterprise clients — model-agnostic, with PII anonymization and full access control.

Book a consultation — 30 minutes to determine which documents should become AI-accessible first.