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Corporate Knowledge Base and RAG Systems

Implementing secure RAG systems: quick search through internal regulations, instructions, and documents. Confidential information does not leak anywhere.

Who it's for and what tasks it fits

  • ✓Companies with complex regulations, numerous procedures, or legal documents
  • ✓Technical support for second and third lines
  • ✓HR departments that are constantly hiring newcomers

What problem the solution solves

People spend dozens of hours each week searching for the right file on Google Drive, asking colleagues, and rereading 50-page regulations. Information becomes outdated. Newcomers take a long time to get up to speed. And mistakes in compliance cost businesses money — and not a small amount.

Capabilities

Semantic search across PDF, DOCX, spreadsheets, and Notion / Confluence databases
Source always indicated: each answer includes a link to a specific document and page
Access rights are restricted: an employee sees only the documents they have access to
Secure contour: documents are not sent for public training of global language models
Vector index updates itself when files change

Implementation and rollout stages

1

Audit and Documentation Cleanup

We analyze the structure of documents, removing outdated versions and inconsistencies.

2

Indexing and Creating Vector Database

We divide texts into meaningful chunks and store them in a vector repository (Qdrant / pgvector).

3

Setting Up Verification Mechanism

We configure queries to LLM with a strict rule: respond only when there is direct confirmation in the provided context.

4

Integration into Workspaces

We connect the assistant to Slack, Telegram, or the team's internal web interface.

Input data and required integrations

  • ·Repositories: Google Drive, Notion, local document archives
  • ·Vector databases: Qdrant, PostgreSQL (pgvector), Pinecone
  • ·Where employees use it: Slack, Telegram, or a private web portal

Automation boundaries and limitations

We tell you honestly upfront what the system can't do. It's easier that way than dealing with false expectations later:

  • ✕RAG will not fix errors that already exist in your regulations
  • ✕Responses are only as quality as the uploaded files are current
  • ✕You need a responsible editor on your side to keep the database up to date

How pricing is calculated

The cost depends on the volume of documentation, the number of access roles, and where the repository will be located: in the cloud or on your server.

Demo example

Corporate Onboarding Assistant

In the first 3–4 weeks, newcomers primarily seek information. How to apply for leave? Where are the passwords for services? Who approves invoices? What is the regulation for responding to clients? Mentors and HR are overwhelmed with the same questions.

View the scenario diagram →

Frequently asked questions about the service

Will our trade secrets leak online?▾

No. We operate through private endpoints or local vector databases. Data there is encrypted, and providers do not use it for retraining general models.

What if the regulation has been updated?▾

The system automatically re-indexes the changed document — via webhook or during scheduled repository synchronization.

How to verify that the answer is true?▾

The assistant always provides a clickable link: the document title, section, and paragraph from where the fact was taken. You can open it and verify.

Let's start with your process: Corporate Knowledge Base and RAG

Describe the process that’s eating up your team’s time. We’ll suggest what can be automated, what data is needed, and where to start.

Your data stays between us. We never ask for trade secrets or access credentials through an open form.

Corporate Knowledge Base and RAG for Business | Neural2B