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RAG and Private AI: how to query enterprise documentation securely

Many companies and public administrations are experimenting with generative AI solutions, but they quickly encounter concrete issues: data privacy, loss of control, unreliable answers, and dependency on external platforms.

Vectoria was created to address these limitations. It is not a generic chatbot and it is not a cloud-based API service. It is an AI solution designed to allow organisations to query their internal documentation through a conversational interface, while maintaining full control over data, models, and infrastructure.

In the IT4LIA AI Factory white paper, Vectoria is presented as a way to bring AI inside the organisation without exposing knowledge, documents, and processes to external providers.

Why enterprise AI requires a different approach

Large Language Models are powerful tools, but they come with structural limitations. They rely on static training data, do not automatically access updated or confidential information, and may generate inaccurate or unverifiable responses.

In an enterprise context, this represents a concrete risk. Knowledge is spread across procedures, manuals, reports, and technical documentation. Sending this content to external AI services raises issues related to confidentiality, compliance, and governance.

Vectoria addresses this challenge by combining generative models with a controlled information retrieval system. Answers are generated exclusively from authorised internal sources.

LLMs and RAG: how Vectoria works

Vectoria is built on a Retrieval Augmented Generation pipeline, commonly referred to as RAG. Instead of relying solely on the model’s internal knowledge, the system retrieves the most relevant content from enterprise documentation and uses it to construct the response.

Documents are processed, cleaned, segmented, and transformed into semantic representations. When a user asks a question, Vectoria identifies the most relevant information and generates an answer grounded in that context.

This approach improves accuracy, reduces hallucinations, and makes responses traceable to their source material. At the same time, it avoids the need to retrain models whenever content changes.

A private AI system by design

One of Vectoria’s defining characteristics is that it operates entirely on private infrastructure. All processing, retrieval, and response generation take place within the organisation’s own environment.

This eliminates the risk of data leakage to external services and reduces dependency on commercial generative AI platforms. Enterprise knowledge remains where it belongs, like any other strategic asset.

For many organisations, especially in regulated sectors or those handling sensitive information, this is not an optional choice but a requirement.

From SMEs to large organisations

Vectoria is designed to adapt to different organisational contexts.

Small and medium-sized enterprises can deploy Vectoria on local servers or workstations, using open-source components and standard hardware. This makes it possible to build a private chatbot without complex infrastructure investments.

Larger organisations can integrate Vectoria with High Performance Computing systems to manage large document collections and more demanding models. The solution is HPC-ready and can scale without changes to the application code.

Vectoria and HPC: scalability when needed

Vectoria can run on standard infrastructure, but it is also designed to take advantage of supercomputing environments.

Using HPC resources makes it possible to process large document repositories, generate embeddings at scale, and run advanced language models that would not be manageable on a single workstation.

This flexibility allows organisations to start gradually and increase system capacity as data volumes and performance requirements grow.

A concrete enterprise use case

EuroCC Italy has published a white paper describing the use of Vectoria at Leonardo S.p.A., where the solution was tested as a RAG-based chatbot to facilitate access to internal documentation.

By indexing procedures and operational documents, Vectoria enabled faster and more accurate information retrieval, reducing search time and improving the experience for both new employees and experienced users.

The pilot demonstrated how a private Retrieval Augmented Generation system can support knowledge management and operational efficiency without compromising data security.

The Vectoria service at IT4LIA AI Factory

IT4LIA AI Factory, in collaboration with EuroCC Italy, supports companies, startups, public administrations, and research organisations in adopting Vectoria as a private AI solution for knowledge access and conversational interaction.

The service covers the entire process, from document ingestion and system configuration to integration with HPC infrastructure when required. The goal is to help organisations deploy AI systems that are controllable, reliable, and sustainable over time.

Download white paper

If you are evaluating a private chatbot or a RAG solution for your organisation but are unsure how to design it in a secure and sustainable way, download the white paper “Vectoria: a private and sustainable AI solution for enterprises”.





    Resources

    RAG and Private AI: how to query enterprise documentation securely

    Many companies and public administrations are experimenting with generative AI solutions, but they quickly encounter concrete issues: data privacy, loss of control, unreliable answers, and dependency on external platforms.

    Vectoria was created to address these limitations. It is not a generic chatbot and it is not a cloud-based API service. It is an AI solution designed to allow organisations to query their internal documentation through a conversational interface, while maintaining full control over data, models, and infrastructure.

    In the IT4LIA AI Factory white paper, Vectoria is presented as a way to bring AI inside the organisation without exposing knowledge, documents, and processes to external providers.

    Why enterprise AI requires a different approach

    Large Language Models are powerful tools, but they come with structural limitations. They rely on static training data, do not automatically access updated or confidential information, and may generate inaccurate or unverifiable responses.

    In an enterprise context, this represents a concrete risk. Knowledge is spread across procedures, manuals, reports, and technical documentation. Sending this content to external AI services raises issues related to confidentiality, compliance, and governance.

    Vectoria addresses this challenge by combining generative models with a controlled information retrieval system. Answers are generated exclusively from authorised internal sources.

    LLMs and RAG: how Vectoria works

    Vectoria is built on a Retrieval Augmented Generation pipeline, commonly referred to as RAG. Instead of relying solely on the model’s internal knowledge, the system retrieves the most relevant content from enterprise documentation and uses it to construct the response.

    Documents are processed, cleaned, segmented, and transformed into semantic representations. When a user asks a question, Vectoria identifies the most relevant information and generates an answer grounded in that context.

    This approach improves accuracy, reduces hallucinations, and makes responses traceable to their source material. At the same time, it avoids the need to retrain models whenever content changes.

    A private AI system by design

    One of Vectoria’s defining characteristics is that it operates entirely on private infrastructure. All processing, retrieval, and response generation take place within the organisation’s own environment.

    This eliminates the risk of data leakage to external services and reduces dependency on commercial generative AI platforms. Enterprise knowledge remains where it belongs, like any other strategic asset.

    For many organisations, especially in regulated sectors or those handling sensitive information, this is not an optional choice but a requirement.

    From SMEs to large organisations

    Vectoria is designed to adapt to different organisational contexts.

    Small and medium-sized enterprises can deploy Vectoria on local servers or workstations, using open-source components and standard hardware. This makes it possible to build a private chatbot without complex infrastructure investments.

    Larger organisations can integrate Vectoria with High Performance Computing systems to manage large document collections and more demanding models. The solution is HPC-ready and can scale without changes to the application code.

    Vectoria and HPC: scalability when needed

    Vectoria can run on standard infrastructure, but it is also designed to take advantage of supercomputing environments.

    Using HPC resources makes it possible to process large document repositories, generate embeddings at scale, and run advanced language models that would not be manageable on a single workstation.

    This flexibility allows organisations to start gradually and increase system capacity as data volumes and performance requirements grow.

    A concrete enterprise use case

    EuroCC Italy has published a white paper describing the use of Vectoria at Leonardo S.p.A., where the solution was tested as a RAG-based chatbot to facilitate access to internal documentation.

    By indexing procedures and operational documents, Vectoria enabled faster and more accurate information retrieval, reducing search time and improving the experience for both new employees and experienced users.

    The pilot demonstrated how a private Retrieval Augmented Generation system can support knowledge management and operational efficiency without compromising data security.

    The Vectoria service at IT4LIA AI Factory

    IT4LIA AI Factory, in collaboration with EuroCC Italy, supports companies, startups, public administrations, and research organisations in adopting Vectoria as a private AI solution for knowledge access and conversational interaction.

    The service covers the entire process, from document ingestion and system configuration to integration with HPC infrastructure when required. The goal is to help organisations deploy AI systems that are controllable, reliable, and sustainable over time.

    Download white paper

    If you are evaluating a private chatbot or a RAG solution for your organisation but are unsure how to design it in a secure and sustainable way, download the white paper “Vectoria: a private and sustainable AI solution for enterprises”.