RAG and LLM integration for answers your team can check
Your policies, service records and operating guidance may be spread across documents and systems. Finding an answer takes time, and a confident response is not useful if nobody can trace it to the right source. A business knowledge application needs reliable retrieval, appropriate access and a clear response when the evidence is insufficient.
Talk to our team
Share a few details and we'll get back to you.
FI Digital helps scope language-model integration and retrieval-augmented generation, or RAG, around those requirements. We start with the question your users need to answer, the information they are allowed to access and the test that will show whether the proposed system is useful.
Start with a question set and a source boundary
Define who will use the system and collect representative questions, including ambiguous requests and questions it should not answer. Identify the documents and records that are authoritative, who maintains them and how users currently find the information.
The first scope should state which sources are included and excluded. An approved service manual and an unreviewed draft should not silently carry the same authority. Missing or conflicting material may require a content-management decision before an engineering change can help.
Prepare information for retrieval
The ingestion process needs to preserve useful context such as document title, version, section and access requirements. We assess file formats, tables, scanned material and update patterns before choosing the extraction and indexing approach. Different source types may need different handling.
The design also covers deletions and replacements. When a policy changes or access is withdrawn, the knowledge application needs a defined way to reflect that change. A successful initial upload does not prove that the information will remain current or correctly restricted.
Keep permissions connected to the answer
Users should receive only information they are authorised to access. We assess how source permissions will be represented and enforced during retrieval, including group membership and changes in access. The application also needs appropriate controls over conversation history and operational logs.
For sensitive workflows, the review should consider what a response could reveal when it combines several records. Permission testing therefore belongs in the acceptance plan, alongside answer quality. The system should be evaluated against attempts to retrieve material outside the user’s approved scope.
Test retrieval and responses separately
When an answer is wrong, the cause may be missing source material, poor retrieval, an outdated document or an unsupported conclusion. Evaluation should distinguish those problems so the remedy addresses the actual failure. Adding a different model is not always the useful next step.
We agree a question set with expected sources and review criteria. Tests should include questions with no supported answer and cases where the correct response is to ask for clarification. Citations help users inspect evidence, but a cited answer still needs to be checked for whether the source supports the statement.
Choose the model for the tested task
Model selection should follow an evaluation of your requirements, including response quality, latency, cost, service availability and data-processing arrangements. Broad claims that one model is always best are not a sound basis for a long-lived business system.
Leave the pilot with a decision
The proposed handover includes the source inventory, access model, evaluation results, known limitations and an operating checklist. It should identify which questions the system can support and which still need a person or a better source. Consumption costs and support responsibilities should be visible.
Start with one bounded knowledge problem and the people who maintain its sources. We can then agree a discovery scope and the evidence needed to decide whether to build, revise the content or use a simpler search workflow.
Evidence from an Australian service engagement
For an anonymous Australian personal injury law firm, we built agents within Zoho Desk using the OpenAI API, Zia agents, custom scripts, RAG and MCP. The firm reported 70% autonomous resolution across approximately 800 incoming service tickets over six months. The implementation took six weeks to go live, with people involved for legal-advice enquiries. This is an Australian delivery reference, not a claim of a UK client or a standard outcome for another firm.
Frequently asked questions
001What is retrieval-augmented generation?
RAG retrieves relevant information from selected sources and provides that material to a language model when generating a response. It can help connect an application to current business information. Its usefulness depends on source quality, retrieval, permissions and evaluation; adding a document store does not automatically make every answer correct or appropriate.
002Will RAG eliminate hallucinations?
No. A model can still misinterpret material, overlook a qualification or produce an unsupported statement. The system needs evaluation, source references and an appropriate response when evidence is missing. Higher-risk uses may require human review. We assess the complete workflow rather than presenting retrieval as a guarantee of error-free output.
003Can different users see different information?
That should be part of the design where source information has access restrictions. We assess identity, group membership, source permissions and how they are enforced during retrieval. Acceptance tests should include both permitted and prohibited requests. Permission changes, logs and conversation history also need consideration throughout the operating life of the application.
004Can the system work with scanned documents and tables?
Potentially, but extraction quality must be tested on representative files. Layout, handwriting, image quality and table structure can affect the result. We assess the source formats before committing to an approach and include difficult examples in evaluation. Some material may need correction, additional processing or exclusion from the initial scope.
005How will it handle a document that changes?
The design should specify how updates, deletions and replacements reach the application, who owns the source and how refresh failures are detected. Version information can help users understand the basis of an answer. The appropriate refresh frequency depends on the business use and should be agreed rather than assumed to be instantaneous.
006Which language model should we use?
Choose through a task-specific evaluation that includes quality, response time, cost, availability and data-processing requirements. General benchmarks can inform an initial shortlist but do not replace tests on representative business questions. Record the selected version and rerun relevant tests when changing the model, retrieval approach or important source material.
007Can we use this for legal or compliance questions?
A knowledge application may help locate and summarise approved material, but its output should not be treated as an automatic legal determination. Scope the use with the responsible advisers, define review requirements and test source accuracy. The workflow should make uncertainty and missing information visible instead of turning a retrieved passage into a compliance guarantee.
008What should an initial RAG assessment deliver?
The agreed scope can produce a source inventory, representative question set, permission model, evaluation approach and implementation recommendation. It should identify unsuitable or missing information and the work required to maintain the system. These outputs let your team decide whether the expected benefit justifies a pilot and what evidence that pilot must produce.
Discuss a business knowledge and RAG assessment
Discuss a business knowledge and RAG assessment→Tell us what you'd like to automate
Share a few details and our engineering team will come back with an honest assessment.
- Reply within 1 business day
- Straight answer on fit, scope and approach
- No obligation, no sales script