Internal knowledge
Help employees find a procedure, product instruction or project agreement. Answers reference the documents used so readers can check the context.
RAG & knowledge assistants
Make your business knowledge searchable with AI.
Manuals, procedures and project documents are often spread across different systems. A knowledge assistant using RAG searches the sources you make available and uses relevant passages to answer questions, with references to those sources.
For internal knowledge questions, document research and employee support. Your sources, access requirements and quality expectations shape the solution.
From searching to understanding
Find answers in your own documents.
RAG stands for Retrieval-Augmented Generation. The application first retrieves relevant information and supplies it to a language model. This helps ground an answer in your business information without retraining a model for that purpose.
Document research
Find relevant passages in a defined collection of reports, manuals or technical documentation. Start with questions where finding the right information is difficult today.
Support
Use approved documentation to draft responses. An employee reviews the result before it goes to a customer or informs a decision.
From source to answer
An information flow you can check.
Select sources
We establish which documents are useful, who may access them and how new or changed content will be processed.
Prepare information
We turn documents into searchable passages, with metadata and references to the original material.
Retrieve relevant passages
The search layer selects information for the question within the user's access permissions.
Check the answer
The assistant presents an answer with sources. It should clearly indicate when there is not enough information.
Quality & limits
Sources and clear agreements matter.
Access and data handling
We discuss hosting, model selection, retention and which information may reach external services. Document permissions must also apply to search results and answers.
The final setup depends on your systems and the providers selected. We document these agreements before connecting business data.
Answers still need review
Outdated sources, unclear questions or missed passages can produce incomplete or incorrect answers. Source references help readers check an answer; they do not guarantee that every conclusion is correct.
Sensitive decisions need human review. Document quality, access permissions and index freshness limit what an assistant can answer.
Our approach
Start with one familiar knowledge problem.
We begin with a defined set of sources and sample questions from your everyday work. These help us assess whether retrieval and answers are useful enough for your application.
Explore
Define users, sources, permissions and expected answers. Include questions the assistant should not answer.
Prototype
Build a limited knowledge assistant and review retrieved passages, source references and answers.
Integrate
Connect the solution to the agreed systems, including authentication, source updates and error handling.
Maintain
Monitor quality and costs, process new documents and improve known weaknesses.
Should the assistant also perform tasks? For actions such as updating a record or starting a workflow, we can combine the knowledge layer with AI agents and system integrations.
Experience from our own software
Connect to a real workflow.
With MailHarbor, we built an application that brings connected mailboxes together and processes invoice attachments on the application server. Original documents can optionally be filed to Google Drive.
This project shows how we connect data sources and everyday tasks in one application. A knowledge assistant additionally needs its own search layer, access model and evaluation of answers.
Which knowledge should be easier to find?
Tell us which documents your employees use, where they live and which questions keep coming up. That gives us a starting point for a useful first application.
Discuss your project