Answers Grounded in Your Documents, With Citations You Can Check
Retrieval-augmented generation that connects a language model to your own knowledge — so answers are sourced from your content, not invented.
What does RAG Knowledge Base involve?
A RAG knowledge base is a system that retrieves relevant passages from an organisation's own documents and supplies them to a language model at query time, so that answers are grounded in source material the user can verify rather than generated from the model's general training alone.
A language model on its own knows nothing about your contracts, your policies, your product documentation or your support history. Ask it a question about your business and it will either decline or, worse, invent a plausible-sounding answer. Retrieval-augmented generation solves this by separating knowledge from reasoning: your documents are ingested, broken into passages, converted into vector embeddings and stored in a search index, and at question time the most relevant passages are retrieved and handed to the model as context. The model then answers using that supplied material, and cites where each claim came from. This is the difference between an assistant that guesses and one that reads the right page of the right document before it replies.
Building a RAG system that works in production is mostly about the parts people do not see in a demo. The ingestion pipeline has to handle the formats your knowledge actually lives in — PDFs, Word documents, Confluence and SharePoint pages, intranet content, support tickets — and keep them current as they change, because a knowledge base that answers from last quarter's policy is a liability. Chunking strategy determines whether retrieval surfaces a coherent passage or a fragment that loses its meaning. Retrieval quality is the single biggest driver of answer quality, so we tune embeddings, combine vector search with keyword search where it helps, and add re-ranking to push the genuinely relevant passages to the top. We enforce access control so the system only ever retrieves documents a given user is permitted to see — a finance policy should not surface in a customer-facing assistant. Every answer is returned with citations linking back to the source, which lets users verify claims and sharply reduces the impact of hallucination because the model is constrained to the material in front of it. We store and serve all of this within Australian regions where data residency and the Privacy Act 1988 require it, and we measure retrieval and answer quality with an evaluation set so the system can be improved deliberately rather than tweaked on instinct.
All Webbed Labs is the enterprise AI and software development arm of All Webbed Up, a Sydney based agency building autonomous systems for Australian businesses.
Why choose All Webbed Labs for RAG Knowledge Base?
Answers Grounded in Your Content
The model answers from passages retrieved out of your own documents, not from its general training. This keeps responses accurate to your policies, products and processes, and means the knowledge base reflects what your organisation actually says rather than a plausible approximation.
Citations on Every Answer
Each response links back to the source document and passage it drew from, so users can verify a claim in one click. Citations build trust, support compliance review, and make the rare incorrect retrieval obvious rather than hidden inside confident prose.
Hallucination Held in Check
Because the model is instructed to answer only from retrieved material and to say so when the answer is not present, fabrication drops sharply. When the knowledge base does not contain an answer, the system says it does not know rather than inventing one.
Always Current Ingestion
Scheduled and event-driven pipelines keep the index in step with your source systems, so when a policy or document changes the knowledge base reflects it. Stale answers are one of the fastest ways to lose user trust, and freshness is engineered in from the start.
Access Control on Retrieval
Document-level permissions are enforced at retrieval time, so the system only ever surfaces content a given user is entitled to see. A customer assistant cannot reach internal documents, and confidential material stays scoped to the people authorised for it.
Measured Retrieval Quality
We evaluate retrieval and answer quality against a curated test set, tuning chunking, embeddings, hybrid search and re-ranking based on evidence. Improvements are measured rather than guessed, so the system gets better in a controlled, repeatable way.
Demo Video
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How do Australian businesses use RAG Knowledge Base?
What technologies does All Webbed Labs use for RAG Knowledge Base?
What does the RAG Knowledge Base process look like?
Knowledge Audit and Source Mapping
We catalogue where your knowledge lives — file shares, Confluence, SharePoint, ticketing systems, PDFs — assess quality and structure, and define which sources are in scope. We also map the access-control model so retrieval permissions mirror your existing entitlements.
Ingestion and Chunking Pipeline
We build pipelines that parse each format reliably, clean and chunk the content so passages stay coherent, and capture metadata for filtering and access control. Scheduled and event-driven refresh keeps the index current as source documents change.
Embeddings, Vector Store and Retrieval
We generate vector embeddings, load them into the vector store best suited to your scale and residency needs — pgvector, Pinecone, Weaviate or Qdrant — and implement retrieval, often combining vector and keyword search with a re-ranking step to surface the most relevant passages.
Answer Generation With Citations
We prompt the model to answer strictly from retrieved passages, attach citations linking back to source documents, and instruct it to say when an answer is not present rather than inventing one. Access control is enforced at retrieval so users only see permitted content.
Evaluation and Retrieval Tuning
We assemble a test set of representative questions with expected sources and answers, then measure retrieval and answer quality and tune chunking, embeddings, hybrid search and re-ranking against the evidence. This evaluation set becomes the gate for future changes.
Deployment, Monitoring and Handover
We deploy within your environment and Australian region where required, set up monitoring for retrieval quality, freshness and usage, and hand over the pipelines, evaluation suite and runbooks so your team can maintain and extend the knowledge base.
Who is RAG Knowledge Base for?
Is RAG Knowledge Base the right solution for you?
When RAG Knowledge Base is the right fit
- Your answers depend on a body of your own documents — policies, manuals, contracts, product or support content
- Users need accurate, verifiable answers with citations back to the source material
- Your knowledge changes over time and a static FAQ or hard-coded responses cannot keep up
- You have access-control requirements that mean different users should see different content
- You have data residency or Privacy Act 1988 obligations that require content to stay within Australia
When it is not the right fit
- Your knowledge fits in a short, stable FAQ — a simple search or static page is cheaper and simpler
- The questions need reasoning over live transactional data rather than documents — that is an analytics or API problem
- Your source content is inaccurate or contradictory; RAG faithfully reflects what it retrieves, so it cannot fix bad source material
- You only need a conversational interface and have no document corpus to ground it in — an AI chatbot without RAG may suffice
- The underlying need is real-time numeric reporting, which is better served by data analytics than retrieval
How much does RAG Knowledge Base cost?
Indicative ranges in AUD to help you budget. Every engagement is scoped individually — book a discovery call for a fixed quote tailored to your requirements.
A single well-defined document set ingested into a vector store with retrieval, cited answers and a starter evaluation set.
Multiple sources with ongoing ingestion, hybrid search and re-ranking, access control, monitoring and a maintained evaluation harness.
Organisation-wide retrieval across many systems, full residency within Australian regions, and integration with multiple downstream assistants.
RAG Knowledge Base: a quick glossary
- Retrieval-Augmented Generation (RAG)
- A technique that retrieves relevant passages from your own documents and supplies them to a language model at query time, so answers are grounded in your content and can be cited, rather than generated from the model's general training alone.
- Vector Embedding
- A numerical representation of a piece of text that captures its meaning, so that passages with similar meaning sit close together in a mathematical space. Embeddings are what make semantic search possible — finding text by meaning rather than exact keywords.
- Vector Store
- A database optimised for storing and searching vector embeddings, such as pgvector, Pinecone, Weaviate or Qdrant. It finds the passages whose embeddings are closest to a query, which is the retrieval step at the heart of a RAG system.
- Chunking
- The process of splitting documents into passages of a workable size before they are embedded. Good chunking keeps each passage coherent and self-contained, which directly improves whether retrieval returns useful, meaningful context.
- Re-ranking
- A second pass that re-orders the passages returned by initial retrieval, using a model that scores relevance more precisely. It pushes the genuinely best passages to the top before they are handed to the language model, improving answer quality.
- Hallucination
- When a model states something false with confidence. RAG reduces it by constraining the model to answer only from retrieved source passages and to say when the answer is not present, rather than relying on its memory.
Common questions about RAG Knowledge Base
A model on its own answers from its general training and has no knowledge of your specific documents, so it cannot reliably answer questions about your policies, products or procedures — and if pushed, it may invent an answer. A RAG knowledge base retrieves the relevant passages from your own content at query time and gives them to the model as context, so the answer is grounded in your material and comes with citations back to the source. In short, the model supplies the reasoning and language; your documents supply the facts.
Yes, substantially, though not to zero. By instructing the model to answer only from the passages retrieved out of your knowledge base and to state when the answer is not present, you remove most of the situations where a model would otherwise fabricate. The remaining risk usually comes from retrieval surfacing the wrong passage rather than the model inventing facts, which is why we measure retrieval quality with an evaluation set and attach citations so any incorrect answer is easy to spot and correct.
The ingestion pipeline is designed for ongoing freshness, not a one-off load. We use scheduled re-indexing and, where your source systems support it, event-driven updates so that when a document changes the index reflects it within a defined window. Documents that are deleted or superseded are removed so the system does not answer from stale material. Freshness is one of the metrics we monitor in production because an out-of-date knowledge base quickly loses user trust.
Yes. Access control is enforced at retrieval time using metadata captured during ingestion, so the system only ever returns passages from documents a given user is permitted to see. This means a customer-facing assistant cannot reach internal material, and confidential documents remain scoped to authorised staff. We map this to your existing entitlement model during the knowledge audit so permissions stay consistent with the rest of your environment.
We can deploy the entire pipeline — vector store, document index and model access — within an Australian cloud region to meet data residency obligations and the Privacy Act 1988. Vector stores such as pgvector, Qdrant and Weaviate can run inside your own environment, and where a hosted model is used we configure zero-retention enterprise terms so your content is never used for training. The full data flow is documented for your compliance and security teams before launch.
We routinely ingest PDFs, Word and PowerPoint documents, HTML and intranet pages, Confluence and SharePoint content, support tickets and structured database records. The ingestion pipeline parses each format, extracts clean text and metadata, and handles the awkward cases — scanned PDFs, tables, mixed layouts — that often degrade retrieval quality if ignored. During the knowledge audit we confirm exactly which sources are in scope and the best way to handle each one.