One of the most common frustrations we encounter when a company begins using artificial intelligence tools is this complaint: «the AI knows nothing about our company». The model replies with generic knowledge, invents procedures that do not exist, or completely ignores the internal documents the team has spent years building. The technical solution to this problem has a name: RAG, an acronym for Retrieval-Augmented Generation. This article explains exactly what it is, how it works, when it makes sense to deploy it, and how it differs from other approaches such as fine-tuning.
What RAG is and why it matters to your business
RAG is an architecture that combines two components: an information retrieval system (a semantic search engine over your own documents) and a large language model (LLM). When a user asks a question, the system first searches the internal knowledge base for the most relevant passages and then passes them to the model as context before generating the answer. The model does not «know» the contents of those documents from memory; it reads them at query time, just as a consultant would if given access to your procedures folder.
The practical result is that the AI answers with real information from your company: the prices in your current rate card, the correct quality procedure, the exact contract clause, the up-to-date figure from the ERP. And it does so citing the source, so the user can verify the origin of every statement.
At Summum IA we deploy RAG systems on technical documentation, customer-support knowledge bases, legal repositories, and any other document corpus within the company, integrating the result into existing workflows.
How RAG works, step by step
The process is divided into two clearly distinct phases: the indexing phase (which happens once, or incrementally when new documents arrive) and the query phase (which happens every time a user asks a question).
Indexing phase: converting documents into vectors
The company's documents (PDFs, Word files, intranet pages, ERP records, historical emails) are split into manageable text chunks, typically between 200 and 500 tokens. Each chunk is converted into an embeddings vector: a mathematical representation of the semantic meaning of the text. These vectors are stored in a vector database (Pinecone, Qdrant, Weaviate, pgvector on PostgreSQL, among others).
The key insight is that this process captures meaning, not just words. If your manual says «warranty period» and a customer asks about «coverage time», the system will correctly associate the two because they are semantically equivalent.
Query phase: retrieve, enrich and generate
- The user's question is vectorised using the same embeddings model.
- The K most similar chunks are retrieved from the vector database using cosine similarity or other metrics.
- The retrieved chunks are included in the prompt sent to the LLM as additional context.
- The model generates the answer based on that context, not on its internal parameters.
- The answer is returned along with references to the source documents.
This complete flow typically completes in under three seconds in well-optimised deployments, making it perfectly viable for customer-service applications, technical support, or real-time internal assistants.
RAG vs fine-tuning: which is right for a mid-sized business?
The most common question when considering how to personalise an AI model is: «is it better to retrain it on our data (fine-tuning) or to build a RAG system?». The answer depends on the use case, but for the vast majority of medium-sized companies, RAG is the right choice.
| Criterion | RAG | Fine-tuning |
|---|---|---|
| Initial cost | Medium (vector infrastructure + integration) | High (GPU, labelled data, iterative process) |
| Knowledge updates | Immediate: just reindex the new document | Requires retraining the model (weeks or months) |
| Transparency and traceability | High: every answer cites the source | Low: knowledge is opaque inside the model weights |
| Hallucination risk | Low when source documents are high quality | Variable; can memorise errors from the corpus |
| Data volume required | Small: any document corpus will do | Large: thousands of labelled examples at minimum |
| Suitable for dynamic data | Yes: prices, stock, changing regulations | No: knowledge is frozen at training time |
| GDPR / AI Act compliance | Simpler: data stays within the controlled corpus | More complex: requires audit of the training dataset |
Fine-tuning remains useful when the goal is to change the model's style (brand tone, very specific output format) or when working with a highly technical and closed domain where the base model lacks sufficient representation. But for «making the AI know what our catalogue says», «having it respond in line with our internal regulations», or «handling support queries based on resolved tickets», RAG is faster, cheaper and more auditable.
Common use cases in mid-sized companies
Internal assistant for procedures and quality management
Companies with certified management systems (ISO 9001, ISO 14001, ISO 45001) accumulate hundreds of procedures, technical instructions and records. A RAG system built on that corpus allows any employee to ask: «what is the goods-receipt procedure?» and receive an answer that includes the procedure number and the current version. Search time drops from minutes to seconds, and the risk of following an outdated procedure disappears.
Customer-service chatbot with product knowledge
An industrial distributor with thousands of product references can index its technical data sheets, installation manuals and spare-parts lists. The assistant answers specific technical questions («what fasteners does model X-220 require?») with exact data extracted from the document, not with generic replies. Customer satisfaction improves and the technical team is freed up for complex incidents.
Legal support and internal compliance
Law firms, insurers, and companies with compliance departments can index their contract library, relevant rulings, internal regulations and corporate policies. Staff query in natural language and receive exact references with clause or article numbers. This use case requires particular attention to access control: not all employees should have access to all documents, so the RAG system must integrate with the organisation's permission structure.
Sales copilot for the commercial team
The sales team can consult interaction histories, terms from previous contracts, or product talking points without leaving the CRM. By connecting the RAG system to the CRM and ERP, a sales representative can ask «what discounts have we applied to this customer over the past 18 months?» and receive a structured answer in seconds.
Technical requirements and privacy considerations
Before deploying a RAG system, several points with both technical and legal implications need to be clarified:
Where is the data processed? If documents contain personal or confidential information, a decision must be made about whether processing happens in the cloud (third-party APIs) or on the company's own infrastructure. With the EU AI Act (Regulation 2024/1689) — which entered into force on 1 August 2024, whose first obligations (prohibited AI systems and AI literacy) have applied since February 2025, and whose most substantial ones (high-risk AI systems) apply from August 2026 — companies deploying AI systems over personal data must document the legal basis for processing under the GDPR and assess the risks of the AI system under the AI Act. A well-designed RAG deployment facilitates this compliance because data stays in the company's vector store and the model only accesses contextual fragments, not the full document.
Quality of the document corpus. RAG amplifies both the quality and the errors of the source documentation. If procedures are out of date or contain inconsistencies, the system will reflect them. Before indexing, it is worth auditing and cleaning the corpus. This document preparation phase is typically the most time-consuming but the most critical for the final result.
Chunking and overlap. The document-splitting strategy directly affects retrieval quality. Chunks that are too short lose context; chunks that are too long saturate the LLM's context window. More advanced strategies include semantic chunking (splitting by paragraphs or logical sections rather than by a fixed token count) and overlap between chunks to avoid cutting ideas in half.
Continuous evaluation. A RAG system is not deployed and forgotten. It requires evaluation metrics: correct retrieval rate, relevance of retrieved chunks, and faithfulness of the answer to the context. Frameworks such as RAGAS (published in 2023 and widely adopted in 2024–2025) provide automated metrics for evaluating RAG systems in production.
Integration with the company's technology ecosystem
A well-integrated RAG system is not a standalone tool; it is an intelligence layer superimposed on existing data sources. The most common connectors include:
- SharePoint and OneDrive: automatic indexing of corporate documents with permission control inherited from Microsoft 365.
- Confluence and Notion: internal knowledge bases for technical or product teams.
- ERPs (Odoo, Sage, Business Central): structured product, customer and operations data via API connectors or ETL.
- Email and calendars: with appropriate privacy restrictions, communication history can enrich context for the sales team.
- Relational databases: via natural-language-to-SQL translation (text-to-SQL), a RAG variant for structured data.
This integration is where the real business value lies, and also where the majority of the implementation work is concentrated. The «connect to an AI model» part is relatively standard; the «connect to the company's real systems with the correct permissions» part requires knowledge of each organisation's specific technology landscape. At Summum IA we approach this work with a RAG deployment service that includes corpus audit, architecture design, connector development and production launch with evaluation metrics.
Frequently asked questions
Do I need to change my AI model to use RAG?
No. RAG is an architecture that works with any language model, whether accessed via API (GPT-4o, Claude 3.5, Gemini 1.5, Mistral) or deployed locally (Llama 3, Mistral, Phi-3). The choice of model depends on cost, privacy and quality criteria, not on the RAG architecture itself. If your company requires that data never leave its own servers, RAG can be combined with an LLM deployed on the company's own infrastructure.
How long does it take to deploy a RAG system?
It depends primarily on the volume and quality of the document corpus and the complexity of the integrations. A RAG system built on a well-structured corpus of 500 to 2,000 documents, with a single query interface and no complex integrations, can be operational in four to eight weeks. Projects involving multiple data sources, granular permission control and integration into existing applications typically require three to five months. The longest phase is usually the preparation and validation of the corpus, not the technical development.
Does the RAG system learn from user conversations?
In the standard architecture, no. The language model does not modify its parameters with each conversation; it simply reads the retrieved context and generates a response. The document corpus is only updated when new or modified documents are reindexed. This is actually an advantage: the system's behaviour is predictable and auditable, and there is no risk of it «learning» incorrect answers introduced by malicious users. If user feedback is to be incorporated to improve the system, this is done through a supervised data curation process, not automatically.
How does the AI Act affect a RAG system in my company?
The AI Act classifies AI systems by risk level. A RAG system used for internal procedure queries or customer support in non-regulated sectors generally falls into the limited or minimal risk category, with basic transparency obligations (informing users that they are interacting with an AI). However, if the system is used in personnel selection, credit assessment, critical infrastructure services or security applications, it may fall into high-risk categories with substantial obligations: registration in the EU database, conformity assessment, technical documentation and human oversight. A case-by-case assessment is essential. For AI Act compliance on the technical side of the system, Summum IA works together with the group's legal and consulting division.