AI Document Search: How RAG Works in Practice

Imagine being able to ask a question of your entire business documentation — contracts, policies, technical manuals — and receive a precise answer in seconds, with the source document and page number cited. This isn't keyword search as you know it from search engines. This is AI document search based on a technique called RAG (Retrieval-Augmented Generation), and it's available today to businesses of all sizes in Montenegro.

In this article, we explain how RAG actually works, step by step, without technical jargon — and why the way data is organized and protected matters just as much as the AI model itself.

What Is RAG and Why Do Companies Use It for Internal Documents?

A standard AI model responds based on knowledge acquired during training. That knowledge is general and becomes outdated the moment the model is deployed — it knows nothing about your internal procedures, contracts, or policies. RAG solves this problem by providing the model, at each query, with relevant excerpts from your documents as context for generating a response.

The result is a system that responds specifically and accurately, referencing what is actually written in your documentation — not generic information. For law firms, accounting firms, hotels with extensive operational documentation, or public institutions with large numbers of policies, this is the practical difference between hours of searching and seconds of finding.

How Does Document Indexing Work?

Before the system can answer questions, documents must be indexed. This phase happens once (and repeats when documents are updated) and goes through several steps.

Step 1: Loading and Preparing Documents

The system accepts documents in various formats — PDF, DOCX, XLSX, scanned documents. For scanned documents, OCR (optical character recognition) is used to convert images into searchable text. Each document is then divided into smaller units, known as passages or "chunks". Metadata is stored alongside each chunk: document name, page number, date, department, or category.

Step 2: Converting Text into Vectors (Embedding)

Each chunk is converted into a numerical vector using an embedding model — a mathematical representation of the text's meaning. Chunks with similar content have vectors that are mathematically close to one another, regardless of whether they use the same words. These vectors are stored in a vector database (ProCode uses pgvector on top of PostgreSQL), which enables fast search by semantic similarity.

Step 3: Classical Index for Exact Matches

Alongside the vector index, the system also builds a classical text index. This index is essential when a user searches for an exact product code, contract article number, or a specific term that must be found literally rather than by similarity. The combination of both indexes forms the foundation of hybrid search.

Hybrid Search: Why One Approach Isn't Enough?

When a user asks a question, the system doesn't rely on just one search method. Hybrid search combines two complementary approaches:

After both approaches return candidates, the system ranks them and selects the most relevant chunks. These chunks are passed to the AI model as context, along with the original question. The model then generates a response — but exclusively based on the provided context, not from general knowledge.

Answers with Cited Sources: Why Does It Matter?

One of the key differences between a RAG system and a generic AI assistant is source citation. Every answer the system generates is accompanied by information about which document and which page the relevant chunk was taken from.

This has practical value on multiple levels. Users can immediately verify the answer in the original document. Management can be confident the system isn't "making things up" — if the answer isn't in the documents, the system clearly states this rather than guessing. For regulated industries — healthcare, law, public administration — the auditability of answers is often a legal requirement as well.

Access Rights: Who Can See Which Document?

This is the aspect most often underestimated when introducing AI document search, yet it can be the most critical. If all documents end up in a single shared index without access control, an employee may receive an answer based on a document they should not be able to see — for example, a confidential contract, HR documentation, or a financial report intended only for management.

A properly built system filters search results according to the rights of the user asking the question. ProCode implements this control through several mechanisms:

• Authentication and identity management via Keycloak — every user logs in with a defined role.
• Search-level filtering — at each query, the system checks which documents the user has access to and searches exclusively that subset.
• Group permissions — documents are assigned to departments, teams, or individuals, and rights are centrally administered.
• Audit trail — every query is logged, enabling subsequent review of who searched for what information.

For companies doing business with EU clients, this is also a matter of compliance with personal data protection regulations. Montenegro's Law on Personal Data Protection is aligned with EU GDPR standards, meaning the same data protection principles apply to AI systems that process business documentation.

A Practical Example: A Law Firm in Podgorica

Imagine a law firm in Podgorica managing hundreds of cases with thousands of pages of contracts, court decisions, correspondence, and internal analyses. A lawyer preparing a submission wants to quickly find all penalty clauses in contracts of a certain type, or all precedents related to a specific legal question.

Without AI search, this means manually searching through folders or relying on memory. With AI document search based on RAG, the lawyer asks a question in natural language — "What are the statutes of limitation in construction contracts from 2022 and 2023?" — and within seconds receives an answer with the exact contracts and articles cited. Junior associates have access only to their team's documents, while senior partners can see the firm's entire documentation.

The same principle applies to hotel chains on the coast managing operational manuals in multiple languages, to distribution companies with extensive product technical documentation, or to local governments with large numbers of regulations and decisions.

Infrastructure and Data Security

For companies where document confidentiality is a priority, the key question is where data is processed. ProCode delivers AI document search as a solution that runs on private EU infrastructure or on-prem — directly on the client's servers. Documents do not leave your infrastructure and are not sent to external AI services.

The technology stack we use — Python, FastAPI, PostgreSQL with the pgvector extension, Docker and Kubernetes for orchestration — enables scalability from a few hundred to millions of documents, with consistent performance. Embedding models are fine-tuned on the client's domain-specific vocabulary, which improves search precision for specialized terminology from a given industry.

If you're interested in what such a solution would look like in your specific case, take a look at our AI solutions and business platforms, which can be integrated with document search into a unified system.

Off-the-Shelf Solution or Custom-Built?

Frequently Asked Questions

How much does AI document search cost?

ProCode AI document search starts from 2,000 €. The final price depends on the number and format of documents, the need for OCR processing of scanned materials, the complexity of access rights, integrations with existing systems, and whether the solution is installed on-prem or on cloud infrastructure. During a free consultation, we can assess the scope of your project.

Can the system work with scanned documents?

Yes. ProCode uses OCR technology that converts scanned pages into searchable text. The quality of scans affects OCR accuracy, but even medium-quality documents can be successfully indexed. For archival materials with lower-quality scans, additional enhancement techniques are available.

How does the system know not to make up answers?

The model is instructed to respond exclusively based on retrieved chunks from your documents. If a relevant chunk does not exist in the index, the system clearly states this — it does not guess. In addition, every answer cites its source, allowing users to immediately verify accuracy in the original document.

How long does it take to implement the system?

It depends on the volume of documentation and the complexity of integrations. For a smaller document set with straightforward access rights, the system can be ready for use within a few weeks. More complex solutions involving ERP or DMS integration, OCR processing of archival materials, and granular access rights typically require one to three months.

Can the system be integrated with our existing software?

Yes. ProCode builds AI document search as a component that integrates with existing business systems — ERP, CRM, and DMS platforms, internal portals, and web applications. Integration is done via API, and authentication can be connected to your existing user management system.

What happens when we add new documents?

New documents are indexed automatically or on a schedule defined by the client. The system does not need to be fully retrained — new chunks are added to the existing index and immediately become available for search. Document version updates can be tracked with a change history, if required.