Building an Internal Knowledge Base with AI: From SharePoint to Smart Search

The Problem With Your Current Knowledge Base

Most organisations have the same problem: years of accumulated documents, policies, procedures, and institutional knowledge scattered across SharePoint folders, Confluence pages, shared drives, and email threads. Someone asks a question, and the answer exists somewhere - but finding it takes 20 minutes of searching, three wrong folders, and eventually a Slack message to the one person who actually knows where things are.

This is not a storage problem. It is a retrieval problem. And it is exactly the kind of problem that retrieval-augmented generation (RAG) is built to solve.

An ai knowledge base built on RAG technology does not replace your existing documents. It sits on top of them, reads them, and gives your team a natural language interface to query everything at once. Ask a question in plain English, get a direct answer with a source citation, and move on with your day.

This article walks through how RAG works in practice, what it takes to build one on top of your existing content, and where organisations typically get it wrong.

How RAG Actually Works

Retrieval-augmented generation combines two components: a retrieval system that finds relevant content, and a language model that synthesises that content into a coherent answer.

The process looks like this:

1. Ingestion - Your documents are processed, split into chunks, and converted into numerical representations called embeddings. These embeddings capture semantic meaning, not just keywords.
2. Storage - The embeddings are stored in a vector database (such as Pinecone, Weaviate, or pgvector in PostgreSQL).
3. Query - When a user asks a question, that question is also converted to an embedding and compared against your stored embeddings to find the most semantically similar chunks.
4. Generation - The retrieved chunks are passed to a language model (such as GPT-4o or Claude) as context, and the model generates an answer grounded in your actual documents.

The critical distinction here is grounding. Unlike a general-purpose chatbot that draws on training data, a RAG system answers from your content. It can cite the specific document and section it used. This matters enormously for compliance, accuracy, and trust.

What You Can Build On Top Of SharePoint

SharePoint is the starting point for most Australian enterprise knowledge base projects, simply because it is already where the content lives. The good news is that SharePoint integrates reasonably well with RAG pipelines. The bad news is that most SharePoint environments are a mess.

Before you connect a language model to your SharePoint, you need to be honest about what is in there. Common issues include:

• Outdated documents sitting alongside current ones with no clear versioning
• Duplicate content across multiple sites and libraries
• Scanned PDFs that contain images of text rather than actual text (these require OCR processing before they can be embedded)
• Inconsistent naming conventions that make it impossible to identify what is current

A practical approach is to start with a content audit of a single department rather than the entire organisation. Pick a team with well-maintained documentation - HR policies, IT procedures, or legal compliance documents work well - and build your first RAG pipeline against that corpus.

For the technical implementation, Microsoft's Graph API provides programmatic access to SharePoint content. You can build an ingestion pipeline that pulls documents, processes them through an OCR layer if needed, chunks them into 500-800 token segments with overlap, generates embeddings using a model like text-embedding-3-large from OpenAI, and stores them in your vector database of choice.

Concrete example: A mid-sized professional services firm in Melbourne built a RAG system on top of their HR SharePoint site, covering 340 policy documents and procedure guides. Before the system, new staff spent an average of 45 minutes per week searching for HR information. After deployment, that dropped to under five minutes. The system also surfaced documents that staff did not know existed, including a flexible work policy that had been uploaded but never properly communicated.

Chunking and Embedding: Where Most Projects Fail

The quality of your ai knowledge base depends heavily on how you chunk your documents. Get this wrong and your retrieval will be poor, regardless of how good your language model is.

Naive chunking - splitting every document into fixed 500-token blocks - works poorly on structured documents like policies and procedures. A chunk might start mid-sentence, contain no useful context about what section it belongs to, or split a table across two chunks that are never retrieved together.

Better approaches include:

• Semantic chunking - Split at natural boundaries like headings, paragraphs, and section breaks rather than fixed token counts
• Hierarchical chunking - Store both a summary chunk and detailed sub-chunks, so retrieval can happen at the right level of granularity
• Metadata enrichment - Attach metadata to each chunk including document title, section heading, last modified date, and author. This metadata can be used to filter results and to construct better citations

For documents with complex structure - tables, numbered lists, multi-column layouts - consider whether your PDF extraction tool is actually preserving that structure. Tools like unstructured.io or pymupdf handle complex document layouts significantly better than basic text extractors.

The embedding model also matters. For Australian enterprise content, models that handle formal business English well are preferable to those optimised for conversational text. OpenAI's text-embedding-3-large and Cohere's embed-english-v3.0 both perform well on professional document content.

Search, Retrieval, and Reranking

Basic vector similarity search retrieves the top-k most semantically similar chunks to a query. This works, but it has limitations. Two chunks might have similar embeddings to the query without actually answering the question. Long documents might have their most relevant section buried in a chunk that ranks fifth.

Production ai knowledge base systems typically use a two-stage retrieval approach:

1. Initial retrieval - Use vector search to pull the top 20-30 candidate chunks
2. Reranking - Pass those candidates through a cross-encoder reranker (such as Cohere Rerank or a locally hosted model) that scores each chunk against the query more precisely

This two-stage approach consistently outperforms single-stage vector search on document question-answering tasks. The reranker is slower and more expensive to run on all documents, which is why you do the cheap vector search first to narrow the field.

You should also consider hybrid search - combining vector similarity with traditional keyword search (BM25). Some queries are better served by exact keyword matching, particularly when users are searching for specific document names, product codes, or policy numbers. Tools like Elasticsearch and Weaviate support hybrid search natively.

Security, Permissions, and Compliance

This is where many RAG implementations create real problems. If your SharePoint has document-level permissions - and most enterprise environments do - your RAG system needs to respect those permissions. A junior staff member should not be able to query the system and receive content from an executive-only document.

This is not a simple problem to solve. Options include:

• Pre-filtering at ingestion - Only ingest documents that all users are permitted to see. Simple but limiting.
• Per-user filtering at query time - When a user queries the system, retrieve their SharePoint permissions via the Graph API and filter the vector search results accordingly. More complex but more correct.
• Separate indexes per permission group - Maintain multiple vector databases segmented by access level. Operationally expensive but straightforward to reason about.

For Australian organisations subject to the Privacy Act 1988 or sector-specific regulations (APRA's CPS 234, for example), you also need to think carefully about where embeddings and document content are stored. Sending sensitive internal documents to a third-party embedding API means that content is leaving your environment. On-premises embedding models or Azure OpenAI (which offers data residency in Australian regions) are worth evaluating for regulated industries.

Evaluating Whether It Is Actually Working

An ai knowledge base is not a set-and-forget system. You need to measure whether it is actually answering questions correctly.

Evaluation approaches include:

• Retrieval evaluation - Given a set of test questions with known answers, does the retrieval step surface the correct document chunks? Measure recall and precision against a manually labelled test set.
• Answer quality evaluation - Does the generated answer correctly reflect the retrieved content? This can be partially automated using an LLM-as-judge approach, where a separate model scores answer faithfulness against the source chunks.
• User feedback loops - Build thumbs up/down feedback into the interface. Over time, this data tells you which query types are performing poorly and which documents are frequently retrieved but apparently not helpful.

Track these metrics over time, particularly after you update your document corpus. A document that was previously accurate might now be outdated, and the system will keep citing it until you either update or remove it.

What to Do Next

If you are ready to move from SharePoint chaos to a working knowledge base, here is a practical starting point:

Start small and scoped. Pick one department with reasonably well-maintained documentation. Build a proof of concept against 50-100 documents rather than your entire SharePoint environment.

Audit your content first. Before you ingest anything, spend a day identifying outdated documents, duplicates, and files that should not be in the system at all. Garbage in, garbage out applies directly to RAG.

Decide on your infrastructure early. Cloud-hosted vector databases and embedding APIs are faster to get started with. On-premises or Azure-hosted options are worth the additional setup time if you are handling sensitive or regulated data.

Plan for maintenance. Your document corpus will change. Build your ingestion pipeline to handle incremental updates - new documents, modified documents, and deleted documents - from day one.

Measure from the start. Define what success looks like before you build. Reduced time-to-answer, fewer "can you find this document for me" Slack messages, or measurable reduction in onboarding time are all concrete metrics worth tracking.

If you want to talk through what a RAG implementation would look like for your organisation's specific content environment, Exponential Tech works with Australian businesses on exactly this kind of project. Reach out at exponentialtech.ai.