Enterprise AI Assistant (RAG)

Let your employees ask their questions; the assistant answers only from documents they are authorised to see, and shows its sources.

Answers with Sources Role-Based Permissions Data Privacy SSO and Audit Logs
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Enterprise AI Assistant (RAG)

Making company knowledge answerable

A company’s knowledge is usually scattered across a few hundred procedures, hundreds of contracts, product manuals, HR policies and years of accumulated e-mail. When a new employee asks “how do I carry over unused leave?”, a sales representative asks “how many months of warranty does this product have?” or a field technician asks “which part do I replace for this error code?”, the answer is either in a senior colleague’s memory or in a folder nobody can find.

An enterprise AI assistant turns these documents into a semantically searchable knowledge base and answers the questions your employees ask in natural language by quoting the relevant documents. It differs from pasting company documents into public ChatGPT in three ways: the data stays under your control, each user sees only the documents they are authorised to see and every answer is based on a verifiable source.

Who is it for?

  • Multi-branch or field teams: teams that need to reach procedures and technical documentation without phoning head office.
  • Departments with heavy regulatory and contract loads: legal, procurement, compliance and quality.
  • HR and internal support teams: human resources, IT help desk and accounting teams who answer the same questions every week.
  • Product and technical support units: teams working with a wide product range, release notes and service manuals.

If you need a customer-facing bot, our Chatbot for Enterprises and AI Customer Service Bot pages cover that scenario. This page focuses on an assistant used by your employees that accesses internal documents.

Components of the assistant

A secure RAG system is not just a connection to a language model; the real work lies in the layers that bring the right document to the right person.

Document ingestion and preprocessing

Scheduled synchronisation from source systems, text recognition, preservation of table and heading structure, and removal of duplicate and outdated document versions.

Chunking and vector index

Documents are split into chunks that preserve meaning, and each chunk is tagged with its source, date and permissions. Semantic search and keyword search are used together (hybrid).

Role and document-level permissions

Search results are filtered by the user’s department, role and permissions in the source system; unauthorised documents are never sent to the model.

Answers that cite their sources

Every answer shows the name, section and link of the document it relies on. If no source is found, the assistant says so instead of guessing.

Personal data masking

Fields such as national ID numbers, IBANs and phone numbers can be detected and masked before indexing or when content is sent to the model.

SSO and user interface

Sign-in with Entra ID, Google Workspace or your corporate identity provider, through a web panel, a Teams/Slack app or a window embedded in your intranet.

Audit log and admin panel

Who asked which question, when, and which documents were used is recorded. Unanswered questions are reported, revealing gaps in your documentation.

Quality measurement

Accuracy measured against a question-and-answer test set prepared by your team, with continuous improvement driven by users’ “helpful / not helpful” feedback.

How do we deploy the assistant? - Enterprise AI Assistant (RAG)

How do we deploy the assistant?

01

Use case and document inventory

We define which teams the assistant will serve and which questions it will answer, then map where the documents that answer those questions are stored and who can access them.

02

Data classification and hosting decision

We classify documents by confidentiality level and decide together with you where the model and index will run. Data transfer and retention periods under KVKK or the GDPR are put in writing at this stage.

03

Building the test set

We collect real questions and the expected correct answers from your teams. This set becomes the acceptance criterion for measuring how well the system works.

04

Pilot deployment

The first version goes live with a limited set of documents and a single team. Chunking, search and answer templates are tuned against the test set.

05

Permission integration and security testing

SSO and role mapping are connected, and test users with different permissions are used to try unauthorised document leakage and prompt injection scenarios.

06

Rollout and maintenance

Other teams and sources are added, synchronisation is scheduled and unanswered questions are tracked through the admin panel.

Hosting options for data privacy

Which option fits depends on your documents’ confidentiality classification and your legal team’s assessment; we make the choice together.
Hosting option Where is data processed? When it fits
Enterprise LLM API + index on your own servers Documents and index stay with you; only the question and relevant chunks go to the API Fast deployment, high answer quality, cases where cross-border transfer can be assessed
Cloud provider’s regional model service In the selected cloud region Companies whose infrastructure is already on a specific cloud and who have a contract with that provider
Fully on-premises (open-source model) No data leaves your organisation Highly confidential documents such as healthcare, finance and public sector; requires investment in GPU servers
Hybrid Confidential documents on the on-premises model, general documents via the API Organisations whose document confidentiality levels are clearly separated
What you have at the end of the project - Enterprise AI Assistant (RAG)

What you have at the end of the project

Deliverables

  • The assistant application, indexing service and synchronisation jobs running in the chosen environment
  • Role-permission mapping table and SSO configuration
  • Test set and measurement results at handover
  • Admin panel: usage, unanswered questions and access logs by source
  • Data flow diagram and a technical description document for your privacy notice and data inventory work under KVKK, the GDPR or local regulations
  • Administrator and end-user training

Pricing

There is no fixed price published on the site for an enterprise AI assistant. The cost is determined by document volume, the number of source systems, the complexity of the permission structure and the hosting option. After a needs assessment meeting, a fixed-price proposal tailored to your project is sent within 24 hours; the development fee and monthly running costs such as model usage and servers are shown separately in the proposal.

If you want to turn the assistant into something that not only answers questions but also gets work done, AI Agents and Business Process Automation can use the same knowledge base. To embed the assistant in your company portal, see Enterprise Portal and Management Systems, and for the infrastructure side, our DevOps Support page.

RAG (Retrieval-Augmented Generation) is an architecture in which, when a question comes in, the relevant passages are first found in your documents and only those passages are given to the model to generate the answer. There is no need to retrain the model (fine-tuning): when a document is updated, the assistant immediately uses the current information, every answer can show which document it is based on, and a document is never retrieved for a user who has no access to it.

The risk cannot be eliminated entirely, but it can be reduced significantly: the assistant is instructed to answer only from the sources it finds and to say clearly when no source is found, and the cited document and section are shown beneath every answer. Before handover, we measure answer quality against a test set of real questions prepared by your team.

The chunked documents and the vector index are kept in the environment you choose: your own servers, a data centre in Turkey or the cloud region of your choice, for example within the EU or the Gulf. For the language model, you can choose an enterprise API (under contract terms that exclude your data from training) or an open-source model running entirely on your own servers. This choice is decisive for your assessment of cross-border data transfers under KVKK (Turkey’s data protection law), the GDPR or your local regulations.

PDF, Word, Excel, PowerPoint, HTML and plain text files; SharePoint, Google Drive, network file shares, Confluence-style wikis and database tables are the most commonly connected sources. For scanned (image) PDFs, a text recognition step is added. If the source system has a permission structure, that structure is carried over into the index.

It can be offered as a web panel, a Microsoft Teams or Slack app, or a window embedded in your existing intranet or portal. Authentication is handled through Azure AD / Entra ID, Google Workspace or your organisation’s SSO solution, so there are no separate passwords to manage.

No. Customer-facing bots work with public information, and brand voice and sales guidance are the priority. An enterprise assistant accesses internal documents, so permissions, audit logs and data leakage safeguards are at its core. For the customer side, see our Chatbot for Enterprises page.

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