AI Knowledge & RAG
Company AI knowledge bases and RAG
The corporate AI knowledge base allows employees to find answers from approved policies, product information, and operational documents. RAG (Retrieval-Augmented Generation) first searches for relevant content and then passes it to the language model to organize the response; document sources, effective versions, and usage rights are conditions that must be confirmed before construction. Vosurein helps organize corporate knowledge, plan retrieval and citation methods, and verify answer quality with real work problems.
For Your Business
Who this service is for and when to start
When product specifications, internal policies, or operational instructions are scattered across folders and different systems, employees cannot find effective versions or repeatedly ask senior staff, a corporate AI knowledge base can be considered. Procurement, sales, administration, and technical support have different needs; the first phase should select the departments and document collections to be used.
For companies that already have a search function but still cannot find answers, it is necessary to examine whether the problem is insufficient categorization, inconsistent keywords, or information hidden in long documents. If data is frequently contradictory or lacks maintainers, content and responsibility should first be organized before deciding the scope of the QA system.
The Challenge
Common challenges faced by businesses
Old and new versions may still mix after document import. For example, the same product may have specifications for different years; if the answer does not identify the date, even referencing the actual documents may give inappropriate advice. OCR errors, lost table relationships, and missing context in attachments will all affect retrieval and interpretation.
Another gap is permission continuation. Data originally only available to specific departments should not be accessible to everyone just because it is centrally stored. When documents are retracted or personnel are transferred, search indexes, cached data, and reference links also need to be handled accordingly.
Our Approach
Methods and applicable requirements
First find the information, then check the basis of the answer.
RAG is an application method combining retrieval and generation, not equivalent to retraining the model. The system first finds relevant document fragments, then generates responses based on the content; during testing, search results and answer quality can be checked separately. After finding the information, it is still necessary to confirm that the content sufficiently supports the answer and that the applicable conditions are correct.
Vosurein evaluates text segmentation, classification fields, and search methods according to document characteristics. Product codes require precise matching, while long descriptions need to retain sections and applicable conditions. The combination to use should be determined based on test results representing the questions.
Implement permission controls when obtaining data.
Document access can be restricted based on user, role, and data classification. Actual implementation must align with the enterprise's identity system, data sources, and search tools. When importing data, permissions should be checked for continuity, and the access scope for different roles should be tested.
We recommend limiting the readable scope each time data is obtained, while also checking reference links. Merely instructing the model through prompts not to disclose confidential information cannot replace system-side authorization checks.
Include versions and no-answer scenarios in testing.
Acceptance checks should cover the retrieved information, whether it supports the answer, and whether conditions and exceptions are complete. If information is insufficient, ask for clarification or refer the question to the responsible staff member. Update indexes and retest affected questions when documents change; maintenance must also handle withdrawn and expired documents.
Process
Consulting scope and process
Review knowledge sources
Confirm users, work-related questions, authorized sources, and document owners. Organize effective dates, product or department classifications, and duplicate versions, and list missing data and the scope of the first phase as the basis for construction.
Retrieval design
Plan document parsing, classification, and searching according to format, while setting authentication, data permissions, and source references. For questions with no answers, unclear conditions, or conflicting sources, define response and manual follow-up procedures.
Implementation and testing
Import agreed-upon data and test with questions that employees are likely to ask. Separately record missing information, outdated versions found, misinterpreted answers, and unauthorized access, with content owners confirming corrections.
Maintenance handover.
Hand over the procedures for adding, updating and withdrawing documents. Agree who reports problems, rechecks quality and reviews usage. Reconfirm permissions and testing scope before adding departments or data sources.
Preparation
What documents do companies need to prepare?
- Document Source: Document samples, formats, storage locations, and valid versions that can be authorized for use; initially, appropriately de-identified samples can be provided.
- Usage Permissions: Departments, roles, and readable scope, as well as login and permission management methods in the original system.
- Q&A Samples: Common questions, acceptable answers, and sources, including questions without answers or needing clarification.
- Maintenance Rules: Content owners, effective and expiration dates, update frequency, and requirements for handling withdrawn documents.
Project Planning
Estimating time and cost
Evaluation focus includes document quantity and quality, scanning recognition needs, table complexity, language, permission levels, source integration, and update frequency. For the same number of documents, the workload of pre-organized content and content that requires re-cleaning may differ; it is advisable to confirm the processing method with samples first.
Costs should be confirmed separately for data organization, system construction, model and search platform usage, storage, and maintenance. The first phase can limit the document set and number of users; after Q&A quality and maintenance processes are stable, further expansion can be evaluated. Effectiveness is judged by test results and usage.
FAQ
Frequently asked questions
How is RAG different from general conversational AI?
RAG includes retrieval results from specified sources before answering, so responses have company data for reference. General chat models may not access the company's latest documents; even after adding retrieval, the applicability of the sources and the response content still need to be checked.
Is RAG the same as model fine-tuning?
No. RAG focuses on obtaining external data when answering; fine-tuning is another method to adjust model behavior. When frequently updating documents or restricting readable range, it is advisable to first evaluate retrieval and permission design, and not directly decide on fine-tuning just because there is a lot of data.
Can we start when documents are not fully organized?
Start with a set of documents that have clear versions and an assigned owner. List missing and conflicting information for follow-up, and limit what the system may answer. The business must still confirm which policies are in force.
Does having a cited source mean the answer is correct?
Not necessarily. The sources may be outdated or only support part of the answer. Acceptance requires checking each source item by item, the applicable conditions, and whether the answer is consistent, and confirming that users can access the original data within their own permissions.
How to prevent different departments from seeing confidential information?
First, define the readable scope for each role, then implement authorization in both retrieval and original document access processes. Testing should involve querying the same question with different roles and checking whether summaries, citations, and historical conversations include unauthorized content.
Will company documents be used to train models?
You cannot judge solely based on using RAG. Training purposes, retention periods, and processing locations need to be verified against the actual vendor, product solution, settings, and contract. When evaluating, clearly state which services the data will go through.
How to update and delete documents after setup?
Synchronized or manual update procedures must follow the source settings, confirming the handling of index and cached data. Deleting the original file does not mean all copies are immediately removed; during handover, the search and answer results after withdrawal must be verified.
Can the knowledge base directly operate ERP or CRM?
Knowledge Q&A and business operations need to be planned separately. If querying cases or writing back data is required, system interfaces, user permissions, field validations, and necessary manual verification must also be confirmed, which can be incorporated into the system integration evaluation.
Related
Related services and enquiries
Please provide document types, user departments, frequently asked questions, and expected implementation time to facilitate discussion on the scope of the first phase.Consult with Vosurein
Content checked: . Applicable versions and requirements depend on the company’s circumstances.
Let's Talk
Start a conversation about your needs.
Tell us how the work is done today and when you hope to finish,
so we can agree the scope and way of working together.
Blog
Sustainability and AI insights
Start with understanding,
and see where change can lead.