What is Enterprise RAG?

Retrieval-Augmented Generation for business use: relevant passages are first retrieved from your sources, respecting access rights, and the language model formulates an answer from them with references.

What do your RAG implementation services include?

Source discovery, ingestion and retrieval, permission mapping and testing, answer evaluation, integration into existing applications, and deployment, maintenance and further development.

Can you connect our existing documents and business systems?

Common document formats are straightforward. Business systems are connected through their interfaces; what that takes is assessed per system and built as part of the project.

How are access permissions handled?

Retrieval filters by the existing permissions of the person asking. We map your access model rather than building a second, parallel one, and test it before go-live.

Can the solution run on-premises?

On-premises operation is an option. Whether it fits depends on model, hardware, security, connectivity and operational requirements, which we assess for your case first.

What infrastructure would we need?

That depends on data volume, users, model choice and deployment. For on-premises operation, suitable compute for the models is usually the deciding factor. We clarify this during planning.

Are our documents used to train models?

No. Your content is used to answer questions, not to train models. Providers and models are agreed with you.

What happens when there is insufficient information?

The system should say so rather than guess. We design answer logic so that missing evidence becomes visible.

How do we start a project?

With a conversation about your questions, sources and requirements. From there we define a focused pilot on an agreed dataset.

AI knowledge management for your business

Turn your data into answers.

UWS connects your documents and business systems so your teams can find answers with traceable sources. We assess the right deployment for your requirements — in a European cloud or on-premises.

Discuss Your Use Case See the Project Example

Example · internal knowledge

Your existing systems
DocumentsCRMBusiness apps
UWS AI Knowledge
Retrieve · Understand context · Cite sources
In everyday work
Answers backed by traceable sources
Right where your team works.
Example source · [1]
Project handbook.pdf
Page 12 · Approval process
The project lead reviews the results before approval.

Permission-based access · Hosted in your environment or Europe

Since 2010
Development, integration, and operations
European engineering team

01Business value

Make your knowledge work for your business.

Put existing knowledge to use in everyday decisions, customer service and project delivery. Help your teams find what is already known and apply it where it matters.

Support informed decisions

Bring relevant information together with sources people can check.

Reduce repeated work

Make documented answers and previous work easier to find and reuse.

Help teams deliver better service

Give employees access to relevant guidance and documentation when they need it.

02The challenge

Your information is spread across systems. Your teams need answers.

AI knowledge management works with what has been written down. Expertise that has never been documented stays with people — making it visible is a separate task, not something retrieval can do on its own.

Scattered documents

Relevant information sits in file shares, emails, business applications and personal folders. Every search starts from scratch.

Search returns files, not answers

Traditional enterprise search produces lists of hits. Reading, assessing and summarising is still left to your staff.

Repeated research

The same questions are researched again because earlier results cannot be found.

Unclear authoritative sources

When several versions exist, it is hard to tell which document is current. Without a reference, an answer cannot be checked.

Permissions must hold

Knowledge access is only usable if it reflects existing access rights instead of bypassing them.

Knowledge that was never captured

Handovers and staff changes expose context that was never written down. Documenting it comes first; a RAG solution can then make it findable.

03Approach

AI knowledge management with Enterprise RAG

Retrieval-Augmented Generation means the language model does not answer from memory. It answers from passages first retrieved from your own sources, and references them. Answer quality depends on source quality, retrieval and evaluation — where evidence is missing, that should be visible rather than guessed.

Connect the sources

Identify and connect the documents and systems relevant to the use case.

Retrieve relevant information

Find useful passages using search methods and access controls appropriate to the source systems.

Generate answers with sources

Use retrieved information to support answers and provide references for review.

04Use cases

Three areas where projects usually start

We typically begin with one use case and a clearly defined dataset. That keeps the first step manageable and shows value early. Knowledge retrieval comes first; autonomous actions and workflow execution are a separate step.

Internal knowledge access

An assistant for policies, manuals, project documentation and recorded procedures. New colleagues find what has already been settled internally.

Research and document analysis

Finding, summarising and comparing information across extensive documents — with references to the underlying passages.

Service and support

Preparing answers from relevant internal documentation. Staff review what reaches customers.

05Our service

Custom RAG development and integration

Discuss Your Use Case

In a first conversation we look at sources, permissions and a sensible first use case — free and without obligation.

Source discovery and integration planning
Document ingestion and retrieval
Mapping and testing access permissions
Answer evaluation and source references
Integration into the applications your teams already use
Deployment, maintenance and further development
Common document formats are established ground; connecting specific business systems is scoped and built per project

06Deployment

Enterprise RAG in your cloud or on-premises

The right deployment depends on your requirements for data residency, connectivity and operations. We decide it together, case by case.

European cloud deployment

Processing in an EU environment with tenant separation, authentication and central logging. Your content is not used to train models. Providers and models are agreed with you and documented.

On-premises deployment

On-premise RAG within your own infrastructure is an option we assess against model, hardware, security, connectivity and operational requirements. We do not assume a ready-made installation.

Operational responsibility

Updates, monitoring, access management and responsibilities are agreed during planning, so it is clear who operates what.

We assess and document remaining risks together with you.

07Project example

From a RAG prototype to a maintainable platform

For a document-intensive environment we built a secure AI knowledge and workflow platform: Retrieval-Augmented Generation, semantic search and agentic functions on existing data sources. A local prototype was developed into a tenant-separated product foundation in an AWS EU environment; development is ongoing.

Starting point: Relevant knowledge was spread across files, emails, business applications and personal folders. The information existed but took considerable time to find and assess.
Implemented: Document and case-file analysis, contextual questions with source references, summaries and further processing of results. Technically: tenant isolation in PostgreSQL with row-level security, JWT authentication, an API gateway, central logging and containerised services.
Planned: Further connectors to business sources, and an action and agent layer that leads from knowledge retrieval to productive workflows.
Ongoing: Following the move from prototype to a product-ready foundation, the platform is being developed continuously. In parallel, we are currently building an assistance system that prepares existing knowledge for use.

Development is AI-assisted, with human control over architecture, security and releases. Regulatory assurance is a separate, ongoing workstream and is not presented here as completed.

08Process

Start with a defined use case

Define the scope

Agree the questions, sources, users and requirements.

Focused pilot

Build a focused pilot using an agreed dataset and handling conditions.

Evaluate

Evaluate answer quality, references and access controls against agreed criteria.

Integrate and expand

Integrate, deploy and expand where the results justify it.

09FAQ

Frequently asked questions about AI knowledge management

Using AI to make documented company knowledge findable and usable — typically by answering questions from your own sources, with references people can check.

Put your company’s knowledge to work.

Tell us which questions your teams need to answer and where the information lives. We will help define a practical starting point.

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