Private AI that works with your data

Artificial intelligence (AI) is no longer just a hope for the future – it has become an everyday tool that helps organizations manage information more efficiently, accelerate decision-making processes, and reduce manual routine work. However, applying AI to sensitive or business-critical documents raises questions about data security, compliance, and control.

The solution is private document-aware AI, which allows artificial intelligence to work with a company’s own files and knowledge without compromising data protection and privacy.

Who is private document-based AI suitable for?

All areas that deal with large volumes of documents, internal knowledge, and unstructured data will benefit from this technology:

Legal field

• Comparison and analysis of contract texts

• Responding based on case law, internal policies, and regulations

• Responding to customer inquiries with the support of legal sources

Finance and audit

• Interpretation of international standards (IFRS, GAAP)

• Creating reports from multiple document sources

• Accelerating audit preparations and preventing risks

Healthcare and pharmaceuticals

• Drug information and research content analysis

• Rapid response to internal treatment guidelines and procedures

• Maintaining privacy when handling sensitive patient data

Industry and manufacturing

• Reviewing safety manuals, maintenance logs and technical inspections

• Resolving technical issues based on documentation

• Internal knowledge retention and accessibility

Research and development

• Analysis of research papers and scientific articles

• Finding connections between topics in large blocks of data

• Supporting working groups with content-based knowledge creation

How does document-based AI work?

The system relies on two key components:

1. Vector-based database (e.g. Qdrant), which allows documents to be searched and ranked semantically.

2. Language model (LLM – Large Language Model), which can generate meaningful, context-sensitive, and understandable responses.

A typical workflow via a third-party API (e.g. OpenAI) looks like this:

• Company files (PDF, DOCX, emails, manuals, etc.) are uploaded to a securely isolated system.

• The contents of the files are converted to vectors (for example, using text-embedding-3-large model) and saved in the local vector bank.

• When a user asks a question, the system finds the most relevant passages from the documents.

• These snippets are passed to a language model (e.g. GPT-4o) via an API to generate a response.

Does an API-based solution mean data leakage?

EiIf the system is configured correctly, the solution connected to the API is also secure. Why?

• Only necessary text segments are transmitted, not the entire document or database.

• OpenAI’s commercial customer data is not stored or used to train modelsif the appropriate privacy settings are set (e.g. data_opt_out).

• The API connection is encrypted. (HTTPS/TLS), so the information is transmitted securely and is not readable by third parties.

• The company’s user interface is secured with user rights, passwords, brute force attack protection, a firewall, and, if necessary, 2FA authentication.

This approach is well suited for companies whose data is sensitive but who do not require complete locality.

What to do if a full inspection is necessary?

If an organization is required to meet strict information security standards (e.g. GDPR, ISO / IEC 27001) or if customer contracts or national requirements require full data localization, then it makes sense to implement completely private installation.

In this case:

• The generation of embeddings and processing of the language model are carried out completely on a local server or in a closed European-based GPU cloud service.

• No data chunks are sent outside the network.

• Answers and inquiries remain completely within the company’s internal network.

• It is possible to implement role-based access, logging, 2FA and workspace differentiation based on usage rights.

Summary

Document-based private AI is not just another technological gadget – it is strategic solution, which can use the organization’s existing knowledge quickly, appropriately and securely.

Companies that:

• work with large databases and documentation on a daily basis,

• want to speed up decision-making processes and reduce information searching,

• need control over the movement and use of data,

can gain a significant competitive advantage with private AI. And while API-connected solutions are secure enough for most, the most demanding can opt for a local installation – completely without compromise.