Chat tools powered by large language models have quickly made their way onto the business agenda. The question on executives' minds is usually the same: "How can we use this technology with our own company knowledge, safely and accurately?"
One of the most common answers is an enterprise AI assistant built on a method called RAG. In this article, without drowning in technical detail, we explain how such an assistant works, where it is useful, what to watch out for and how to get started.
What is an enterprise AI assistant?
An enterprise AI assistant is software that lets employees or customers ask questions in natural language and receive answers based on company-specific information. Behind the scenes there is usually a large language model (LLM): an AI model trained on vast amounts of text that can generate human-like language.
A general-purpose language model knows the general information contained in its training data, but it does not know your company's internal procedures, product documentation, contract templates or current price list. What makes an enterprise assistant valuable is that it closes this gap in a controlled way.
How does RAG work?
RAG (retrieval-augmented generation) is a method that has the language model search the company's own documents before it generates an answer. Instead of retraining the model on company data, it finds the relevant information for each question and supplies it to the model as a source.
In simplified form, the process works like this:
- Preparation: company documents are split into small, meaningful chunks. Each chunk is converted into a numerical representation of its meaning (an embedding) and stored in a search index.
- Question: the user asks the assistant a question in natural language.
- Retrieval: the system finds the document chunks most relevant in meaning to the question. This search relies not only on keyword matches but also on semantic similarity.
- Generation: the retrieved chunks are passed to the language model together with the question, and the model is instructed to answer based only on that information.
- Citing sources: the answer shows which document it drew on, so the user can open the original document and verify it when necessary.
A key advantage of this approach is that the model does not need to be retrained when documents are updated; updating the search index is enough. It is also possible to restrict which documents are searched according to each user's permissions.
Where can it be used?
Internal knowledge base
Scattered information such as HR policies, procurement procedures, technical documentation or quality manuals becomes accessible through a single question-and-answer interface. Onboarding new employees and answering frequently repeated internal questions are typical examples in this area.
Customer support
An assistant working from product manuals and FAQs can suggest draft replies to support teams or answer customers' simple questions directly. In customer-facing scenarios, it is important that the assistant can hand the conversation over to a member of staff when it cannot answer or when the topic is sensitive.
Document search and summarisation
Finding the relevant section in long documents such as contracts, technical specifications, tender documents or past project reports takes time. A RAG-based search can answer a question like "What are the termination conditions in this contract?" by pointing to the relevant clause.
Data preparation: an assistant is only as good as the information it is fed
The most important factor in the success of a RAG project is often not the choice of model but the quality of the data. Outdated, contradictory or scattered documents lead the assistant to give wrong or inconsistent answers as well.
- Source inventory: which documents live in which systems (file server, document management system, intranet, ERP)?
- Clean-up: old versions, drafts and contradictory documents should be identified.
- Ownership: a department or person should be made responsible for keeping each group of documents up to date.
- Format: scanned documents should be converted to text, and tables and heading structure should be preserved.
- Permissions: whoever is allowed to access a document should be enforced in exactly the same way through the assistant.
- Updates: plan how and how often the search index will be refreshed when documents change.
Data privacy, KVKK and model selection
If the documents given to the assistant contain personal data, the processing falls under Türkiye's Personal Data Protection Law No. 6698 (KVKK), the country's counterpart to the EU's GDPR. It must be clear for what purpose the data is processed, where it is stored, who can access it and how long it is retained. The questions users type into the assistant and the conversation logs are also part of this assessment.
If a cloud-based model service is to be used, you should review where the provider processes data, whether it uses submitted data to train its models, and the terms of the contract. Transfers of personal data outside Türkiye are separately regulated under KVKK, so legal and information security teams need to be involved in the process from the start.
Cloud or on-premise?
- Cloud-based models: offer a quick start and require no infrastructure management. On the other hand, data is processed on the provider's infrastructure.
- On-premise models: open-weight models, meaning models whose files are publicly released, can be run on the company's own servers. Data never leaves the company, but responsibility for hardware, installation and maintenance lies with the company.
- Hybrid set-ups: sensitive documents can be processed in-house while cloud services are used for less sensitive scenarios.
The right choice depends on how sensitive the data is, your legal obligations, the expected volume of use and the company's infrastructure capabilities.
Hallucination and quality measurement
Language models sometimes produce answers that sound fluent and convincing but are wrong. This is known as hallucination. By grounding the model in company documents, RAG reduces this risk but does not eliminate it entirely. Quality therefore needs to be measured continuously:
- Instructing the model to answer only on the basis of the sources provided, and to say so clearly when it cannot find the information
- Showing the source document and the relevant section with every answer
- Building a test set of real user questions whose correct answers have been determined by subject-matter experts
- Re-evaluating the system against this test set after every significant change
- Setting up a feedback mechanism that lets users mark answers as correct or incorrect
- Leaving the final say to a human in critical legal, financial or health-related decisions
Starting with a pilot project
The soundest way to introduce an enterprise AI assistant is to begin with a limited, measurable pilot rather than a large project covering the whole company.
- Choose a single use case; for example, employee questions about HR policies.
- Define the scope: which documents will be included, and which user group will try the assistant?
- Define success criteria in advance, such as answer accuracy, user satisfaction and time to find information.
- Prepare the data, and complete the KVKK and information security assessment.
- Run the pilot for a limited period, collect feedback and measure the results against the test set.
- Based on the results, decide whether to expand the scope, improve the solution or stop.
A pilot shows not only whether the technology works, but also how ready your data is and how users respond to the tool.
Conclusion
A RAG-based enterprise AI assistant is a practical way to make company knowledge more accessible. Success, however, depends on well-prepared data, addressing privacy and KVKK requirements from the outset, regular quality measurement and moving forward in small steps.
If you would like to identify a suitable pilot scenario for your company and plan how the assistant will integrate with your existing systems, the Linu Technology team can help you through the process.