Retrieval-Augmented Generation (RAG) Solutions
Large language models can create useful answers, but they do not automatically understand your organisation’s documents, policies, products, or internal processes. Without access to trusted business information, responses may be too general, outdated, or factually incorrect.
Retrieval-Augmented Generation, commonly known as RAG, connects a language model with approved information sources. When a user asks a question, the solution retrieves relevant content and uses it to produce a more accurate and context-aware response.
RAG Solutions from CA help you make business knowledge easier to find and use. We design secure solutions that connect generative AI with your existing documents and systems, while maintaining appropriate control over access, data handling, and the sources used to generate answers.
Why This Matters
Many organisations have large amounts of valuable information spread across document libraries, internal portals, shared folders, knowledge bases, and business applications. Employees may know that the information exists but still spend considerable time searching for it, checking whether it is current, or asking colleagues for help.
A general-purpose language model cannot solve this problem on its own. If the model is not connected to reliable organisational information, it may provide a plausible answer that does not reflect your actual policies, services, or working practices.
A well-designed RAG solution helps you:
Improve access to business knowledge
Allow employees and customers to ask natural-language questions and receive answers based on approved information sources.
Increase the reliability of answers
Ground generated responses in relevant documents instead of relying only on the model’s general knowledge.
Protect access to sensitive content
Ensure that users only receive information they are authorised to view.
Reduce repetitive work
Help teams spend less time searching through documents or responding repeatedly to common information requests.
Our Approach
A successful RAG solution requires more than uploading documents and connecting them to a model. The quality of the answers depends on the quality, structure, relevance, and accessibility of the underlying information.
We consider the complete information flow, from source selection and document preparation to retrieval, response generation, access control, testing, and monitoring. The objective is to create a solution that provides useful answers while remaining secure, transparent, and manageable.
Define the use case and information sources
We begin by identifying the questions the solution should answer, the users it should support, and the business outcomes it should deliver.
We then determine which information sources should be included. These may include policies, technical documentation, product information, service procedures, customer materials, knowledge bases, or records stored in business systems.
We also review the quality, ownership, sensitivity, and update frequency of the content. This helps prevent outdated, duplicated, incomplete, or unauthorised information from becoming part of the solution.
Prepare and structure the knowledge
We prepare the selected information so that it can be retrieved effectively. This may include removing duplicates, improving document structure, dividing content into appropriate sections, adding metadata, and defining relationships between documents.
The way information is prepared has a direct impact on the quality of the responses. If documents are poorly organised or divided without considering their meaning, the system may retrieve incomplete or irrelevant content.
We design the knowledge structure around the intended questions and business context rather than applying the same technical configuration to every type of information.
Build and secure the retrieval process
We connect the approved information sources to the selected language model through a controlled retrieval process.
The solution identifies content that is relevant to a user’s question and provides it to the model as context for the response. We configure search methods, relevance criteria, access controls, source references, and safeguards against inappropriate information retrieval.
Where information has different confidentiality levels, the retrieval process can respect existing identities and permissions. This helps ensure that access through the AI solution does not bypass the controls already applied to the original documents or systems.
Test, improve, and monitor the solution
We test the solution using realistic questions, including common requests, specialist queries, ambiguous wording, incomplete information, and questions that should not be answered.
We assess whether the correct content is retrieved, whether answers remain consistent with the source material, and whether references are clear and useful. We also test access restrictions, the handling of outdated information, and the response produced when reliable content cannot be found.
After deployment, we help monitor response quality, search performance, user feedback, content changes, and recurring failures. This supports continuous improvement as the organisation’s information and requirements develop.
What Sets CA Apart
Business knowledge comes first
We begin with the information users need and the decisions the solution should support, not simply with the available technology.
Security-aware retrieval
We design access controls and retrieval rules so that the AI solution respects the confidentiality of organisational information.
Testing based on real questions
We evaluate the solution using realistic user requests rather than relying only on technical demonstrations.
Transparent and manageable answers
We support clear references to source material and define how the solution should respond when suitable information is unavailable.
The Business Value
RAG Solutions from CA help your organisation turn existing information into a more accessible and useful business resource.
Employees can find relevant answers more quickly, customer-facing teams can respond more consistently, and specialists can spend less time handling repeated information requests. Responses are based on approved sources, making them more relevant to the organisation than answers generated from general model knowledge alone.
A structured RAG solution also gives the organisation greater control over how generative AI uses internal information. Access permissions, source selection, data flows, and response quality can be managed and reviewed.
The result is not simply an AI search tool. It is a practical way to improve knowledge sharing, support faster decisions, and help people use trusted information more effectively in their daily work.
Next Step
Your organisation may already have the knowledge people need. The challenge is making that knowledge easier to find, understand, and use.
Contact CA for an initial consultation. We will help you identify the right information sources, design a secure retrieval process, and develop a RAG solution that provides reliable answers based on your business knowledge.