--- title: "RAG and knowledge systems | Quantlix" description: "Company knowledge systems and RAG with approved sources, permission-aware retrieval, cited answers and evaluation. Scope source ownership and maintenance." canonical: "https://quantlix.com/en/services/rag-knowledge-systems/" last-updated: 2026-10-09 --- Source: https://quantlix.com/en/services/rag-knowledge-systems/ Language: en Description: Company knowledge systems and RAG with approved sources, permission-aware retrieval, cited answers and evaluation. Scope source ownership and maintenance. # RAG and knowledge systems Company knowledge systems and RAG with approved sources, permission-aware retrieval, cited answers and evaluation. Scope source ownership and maintenance. [Discuss your project](https://quantlix.com/en/contact/) [Explore the service](https://quantlix.com/en/services/rag-knowledge-systems/#service-scope) ![Network illustration with the label RAG](https://quantlix.com/_astro/rag.CUnbr-KJ_20opy2.webp) Illustrative image. ## A clear purpose. A practical approach. ### Where this service fits Retrieval-augmented generation, or RAG, retrieves relevant source material before a language model prepares an answer. Quantlix scopes company knowledge systems around approved documents, ownership, permissions, and source references. The first task is often to decide which procedure is current and who can correct it, before introducing an assistant. ### Workflow and acceptance criteria Illustrative workflow: a maintenance engineer asks for a procedure for one machine. Retrieval must distinguish similar equipment, filter restricted documents, and identify the approved revision. Evaluation separates finding the correct source from producing a supported answer. Include unanswered questions, conflicting instructions, withdrawn documents, and revoked access. A citation must support the answer; its presence alone is insufficient. Freshness and permission changes need an operating owner. ### What to bring to discovery A knowledge assistant depends on the quality and accessibility of its sources. Quantlix tests retrieval and answer quality separately to identify the real constraint. Bring representative documents and questions, including questions the system should decline. Define who owns source updates and permission changes. ## Where we can help A focused scope, shaped around your priorities. ### Knowledge preparation Review document structure, freshness, ownership and permissions. ### Retrieval design Combine search, ranking and context selection for real questions. ### Grounded answers Show sources and evaluate unsupported or incomplete responses. ## Knowledge with a traceable source Retrieve authorized material before generating an answer with references. - Approved sources - Retrieval - Language model - Cited answer ## What the work puts in your hands Agree on useful, reviewable deliverables before the work begins. - Knowledge inventory - Retrieval prototype - Question evaluation set - Update strategy ### Reference points These resources inform the conversation. Applicable requirements are agreed for your project. - [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) - [OWASP ASVS](https://owasp.org/projects/asvs) ## Good work starts with a shared understanding. Very small PODs bring together an expert in the specific domain and engineers selected for relevant prior industry experience. The team connects operational context with software decisions. Responsibilities, collaboration and support are agreed for each engagement. Each engagement starts with an agreed scope and a project-specific quote. [How we scope and price projects](https://quantlix.com/en/pricing/) ### Practical guides for the next decision - [How to choose an industrial AI partner](https://quantlix.com/en/blog/choose-industrial-ai-partner/) - [RAG, fine-tuning, or workflow automation?](https://quantlix.com/en/blog/rag-fine-tuning-workflow-automation/) - [A readiness checklist for long-running AI agents](https://quantlix.com/en/blog/long-running-ai-agent-readiness/) ### Start with the real problem Discuss the people, business goals and constraints behind the request. Agree on the scope and what a useful outcome looks like. ### Make the direction tangible Use working sessions, research and early drafts to explore the options. Review the tradeoffs with your team before committing to a direction. ### Work in reviewable increments Bring the agreed work into focus through regular reviews. Document the decisions, hand over the deliverables and plan any ongoing support. ## Questions, answered The practical details to consider before starting. **How does RAG differ from fine-tuning?** RAG retrieves source material at request time. Fine-tuning adapts model behavior through training examples. **Can users see restricted documents?** Retrieval must apply source permissions before presenting material to the model or user. **Does RAG eliminate incorrect answers?** Retrieval can support grounding, but answer correctness still requires evaluation, useful sources and fallback behavior. ## Connect the right services Explore the work that can support the next part of your project. [View all services](https://quantlix.com/en/services/) ![Circuit traces forming a human head on a blue background](https://quantlix.com/_astro/artificial-intelligence.z-Hurd7C_Z2l7A16.webp) ### [Artificial intelligence](https://quantlix.com/en/services/artificial-intelligence/) Industrial AI for inspection, maintenance and operational decision support. Quantlix scopes data, integration, evaluation and human review around one workflow. ![Laptop beside printed charts on a desk](https://quantlix.com/_astro/data-analytics.DsZ_dvPM_Z1g9Jo7.webp) ### [Data analytics](https://quantlix.com/en/services/data-analytics/) Quantlix builds analytics pipelines and business intelligence tools that turn operational data into defined metrics, useful reports and decision support. ![Circuit illustration with the label Generative AI](https://quantlix.com/_astro/generative-ai.w0LRubVo_28mHUX.webp) ### [Generative AI solutions](https://quantlix.com/en/services/generative-ai/) Quantlix builds generative AI workflows for drafting content and creating assets, with brand guidance, review controls and integration into publishing processes. ## Let’s talk about what needs to change. Bring your challenge, your questions and your starting point. We’ll work out the next step together. [Discuss your project](https://quantlix.com/en/contact/)