Back to Blog

How to Build AI-Powered Search for Your Business Data

April 24, 2026•11 min read•Michael Ridland

Your employees search for information dozens of times a day. They search SharePoint and find 200 results they have to sift through. They search the CRM and get partial matches. They search the knowledge base and give up after three minutes. Then they message a colleague to ask a question that's answered in a document somewhere.

AI-powered search changes this. Instead of returning a list of documents that might contain the answer, it returns the answer - sourced from your actual business data, with citations you can verify.

I'm Michael Ridland, founder of Team 400, and we build AI-powered search systems for Australian organisations. Here's how to approach it.

How Is AI Search Different from Traditional Search?

Traditional enterprise search is keyword-based. You type "return policy enterprise customers" and it finds documents containing those words. If the document says "refund guidelines for corporate accounts" instead, you miss it.

AI-powered search understands meaning, not just words. It works through a technique called semantic search, which converts text into numerical representations (vectors) that capture the meaning of the content. Similar meanings produce similar vectors, so "return policy enterprise customers" matches "refund guidelines for corporate accounts" because the meaning is close.

But semantic search alone isn't enough for enterprise use. The best AI search systems combine multiple approaches.

Keyword search - Still essential for exact matches. Product codes, names, reference numbers, policy identifiers. When someone searches for "INV-2026-0847", you need exact matching.

Semantic search - For meaning-based matching. When someone asks "how do we handle late deliveries", semantic search finds relevant content regardless of the exact wording used.

Hybrid search - Combines keyword and semantic search. This consistently outperforms either approach alone across every benchmark we've tested.

Generative answers - After finding the relevant content, an LLM reads it and generates a direct answer. This is the RAG (Retrieval-Augmented Generation) pattern.

Traditional: "return policy" → 47 documents → User reads through them

AI Search: "what's our return policy for enterprise customers?"
  → Hybrid retrieval finds 5 most relevant passages
  → LLM generates: "Enterprise customers can return products within 60 days
    for a full refund. Custom items have a 15% restocking fee.
    [Source: Enterprise Sales Policy v4.2]"

The Architecture

Here's what a production AI search system looks like.

Data Layer

Your business data sits in multiple systems. The first challenge is getting it all into a searchable format.

Common data sources:

  • SharePoint and OneDrive (documents, lists, pages)
  • Databases (SQL Server, PostgreSQL, Cosmos DB)
  • CRM systems (Salesforce, Dynamics 365)
  • Knowledge bases (Confluence, internal wikis)
  • File shares
  • Email archives
  • ERP systems (SAP, NetSuite)
  • Custom applications

Each source needs a connector that extracts content and metadata.

Indexing Pipeline

Data Sources → Connectors → Text Extraction → Chunking → Embedding → Index
                                                              ↓
                                                    Azure AI Search
                                                    (Vectors + Keywords + Metadata)

Text extraction pulls readable text from documents. For PDFs, this means OCR for scanned documents. For databases, it means serialising records into searchable text. For web applications, it means API calls.

Chunking splits content into smaller pieces. A 50-page policy document gets split into sections of 500-1,500 tokens each. This is important because:

  • Smaller chunks enable more precise retrieval
  • The LLM's context window is limited
  • Relevance scoring is more accurate on focused chunks

Embedding converts each chunk into a vector using a model like Azure OpenAI's text-embedding-3-large. Each vector is typically 3,072 dimensions and captures the semantic meaning of the text.

Indexing stores vectors, text, and metadata in a search engine optimised for this purpose.

Query Pipeline

User Query → Query Understanding → Hybrid Retrieval → Reranking → Generation → Answer

Query understanding (optional but valuable) rewrites the user's query for better retrieval. For example, expanding abbreviations, resolving references from conversation history, or splitting complex queries into sub-queries.

Hybrid retrieval runs both keyword and vector search against the index and merges the results.

Reranking uses a second model to re-score the merged results for relevance. This step typically improves accuracy by 10-20%.

Generation sends the top results plus the user's question to an LLM, which produces a grounded answer with source citations.

Implementation Guide

Step 1 - Choose Your Search Platform

Azure AI Search is our default recommendation for Australian enterprise. Here's why:

  • Native hybrid search (keyword + vector in a single query)
  • Built-in semantic ranker for reranking
  • Runs in Australian datacentres
  • Integrates directly with Azure OpenAI
  • Supports complex filtering (security, metadata)
  • Scales to millions of documents

Alternatives:

  • Elasticsearch with vector plugin - Good if you already run Elasticsearch
  • PostgreSQL with pgvector - Cost-effective for smaller datasets (under 1 million chunks)
  • Pinecone - Managed vector database, simple to use, but data is hosted in the US

For most Australian organisations with data residency requirements, Azure AI Search is the clear choice.

Step 2 - Design Your Index Schema

Your index needs to support keyword search, vector search, and metadata filtering.

{
    "name": "business-data-index",
    "fields": [
        {"name": "id", "type": "Edm.String", "key": true},
        {"name": "content", "type": "Edm.String", "searchable": true},
        {"name": "content_vector", "type": "Collection(Edm.Single)",
         "dimensions": 3072, "vectorSearchProfile": "default"},
        {"name": "title", "type": "Edm.String", "searchable": true, "filterable": true},
        {"name": "source_system", "type": "Edm.String", "filterable": true, "facetable": true},
        {"name": "source_url", "type": "Edm.String"},
        {"name": "last_updated", "type": "Edm.DateTimeOffset", "filterable": true, "sortable": true},
        {"name": "department", "type": "Edm.String", "filterable": true, "facetable": true},
        {"name": "document_type", "type": "Edm.String", "filterable": true, "facetable": true},
        {"name": "allowed_groups", "type": "Collection(Edm.String)", "filterable": true}
    ]
}

Key design decisions:

  • Content field is searchable for keyword matching
  • Content vector enables semantic search
  • Metadata fields enable filtering (by source, date, department)
  • Allowed groups enables security trimming

Step 3 - Build Data Connectors

For each data source, build a connector that extracts content and metadata.

SharePoint connector:

Use Microsoft Graph API to enumerate sites, libraries, and documents. Azure AI Search also has a built-in SharePoint connector, but building your own gives more control over extraction and chunking.

Key considerations:

  • Handle document permissions (who can access what)
  • Support incremental sync (only process changed documents)
  • Extract text from all file types (PDF, DOCX, PPTX, XLSX)

Database connector:

For SQL databases, query records and convert them to searchable documents.

# Example: Indexing product records from a database
products = db.execute("SELECT * FROM products WHERE updated_at > ?", [last_sync])

for product in products:
    searchable_text = f"""
    Product: {product.name}
    Category: {product.category}
    Description: {product.description}
    Specifications: {product.specifications}
    Price: ${product.price}
    SKU: {product.sku}
    """

    embedding = embed(searchable_text)
    index_document(product.id, searchable_text, embedding, product.metadata)

CRM connector:

CRM data is often the most valuable for search but also the most sensitive. Extract customer records, opportunities, case histories, and notes. Apply strict access controls.

Step 4 - Implement Hybrid Search

Azure AI Search supports hybrid queries natively.

from azure.search.documents import SearchClient
from azure.search.documents.models import VectorizedQuery

# Embed the user's query
query_embedding = azure_openai_client.embeddings.create(
    model="embedding-deployment",
    input=user_query
).data[0].embedding

# Run hybrid search
results = search_client.search(
    search_text=user_query,  # Keyword search
    vector_queries=[
        VectorizedQuery(
            vector=query_embedding,
            k_nearest_neighbors=50,
            fields="content_vector"
        )
    ],
    filter=security_filter,  # Only documents user can access
    top=10,
    query_type="semantic",  # Enable semantic ranker
    semantic_configuration_name="default"
)

This single query does keyword matching, vector similarity, and semantic reranking in one call.

Step 5 - Build the Answer Generation Layer

Take the top search results and generate an answer using Azure OpenAI.

# Prepare context from search results
context_chunks = []
for result in results:
    context_chunks.append(
        f"[Source: {result['title']}]\n{result['content']}"
    )

context = "\n\n---\n\n".join(context_chunks)

# Generate answer
response = azure_openai_client.chat.completions.create(
    model="gpt-4o-deployment",
    messages=[
        {"role": "system", "content": """
            Answer the user's question based ONLY on the provided context.
            If the context doesn't contain enough information, say so.
            Always cite the source document for each claim.
            Use Australian English.
        """},
        {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {user_query}"}
    ]
)

Step 6 - Build the User Interface

The interface should support both search and conversation.

Search results view:

  • Show the generated answer prominently
  • List source documents below with relevance indicators
  • Allow users to click through to the original document
  • Provide thumbs up/down feedback buttons

Conversational view:

  • Support follow-up questions that build on previous context
  • Maintain conversation history
  • Allow users to refine or redirect their search

Filters and facets:

  • Let users filter by source system, department, document type, date range
  • Show facet counts so users understand the scope of results

We typically build this as a web application embedded in your intranet or as a Teams bot for maximum accessibility.

Performance Optimisation

Query Latency

Target response time: under 3 seconds for search results, under 5 seconds for generated answers.

Speed up retrieval:

  • Use Azure AI Search's built-in caching
  • Optimise index schema (don't index fields you don't search on)
  • Use appropriate tier (Standard S2+ for large indexes)

Speed up generation:

  • Use GPT-4o-mini for straightforward queries, GPT-4o for complex ones
  • Stream the response so users see content appearing immediately
  • Limit context to the top 5-8 most relevant chunks

Index Freshness

Users lose trust quickly if search returns outdated information.

Real-time updates: For critical systems, use webhooks or change feeds to update the index within minutes of a change.

Scheduled sync: For less time-sensitive sources, sync every 1-4 hours.

Manual refresh: Let admins trigger a re-index for specific sources when needed.

Track index freshness as a metric. If the average document in your index is 12 hours old, users might find stale information.

Scaling to Large Datasets

Azure AI Search scales well, but keep these in mind:

Scale Recommendation
Under 100K chunks Standard S1 tier, single partition
100K - 1M chunks Standard S2, 2-4 partitions
1M - 10M chunks Standard S3 or S3 HD
Over 10M chunks Multiple indexes with routing

Embedding cost also scales. At 10 million chunks with text-embedding-3-large, initial embedding costs roughly $130 AUD. Re-embedding during updates is incremental.

Security - Getting It Right

Security is not optional for enterprise search. Your AI search system has access to sensitive business data, and the answers it generates must respect who's asking.

Security trimming at query time. Every query must filter results based on the current user's permissions. This means:

  1. During indexing, capture and store document permissions
  2. At query time, look up the user's group memberships
  3. Apply a filter to only return documents the user can access

Row-level security for database data. If you're indexing CRM or ERP data, apply the same access controls your source system enforces.

Audit logging. Log every query and the documents returned. You need to be able to answer "who searched for what and what did they see."

Content classification. Some documents may be too sensitive for AI search (board minutes, HR investigations, M&A documents). Provide a mechanism to exclude specific documents or libraries from indexing.

Measuring Success

Track these metrics to know if your AI search is working.

Search quality:

  • Answer accuracy (regular evaluation against test questions)
  • User satisfaction rate (thumbs up/down)
  • Zero-result rate (queries that return nothing useful)

Usage:

  • Daily/weekly active users
  • Queries per user per day
  • Most common query topics
  • Repeat queries (users asking the same thing multiple times suggests the answer wasn't satisfactory)

Operational:

  • Query latency (p50, p95)
  • Index freshness
  • Token cost per query
  • Monthly infrastructure cost

Business impact:

  • Time saved finding information (survey-based)
  • Reduction in "who knows about X?" messages
  • Support ticket deflection (if applicable)

Common Pitfalls

Indexing everything at once. Start with one or two high-value data sources. Get search quality right, then expand. A search engine that covers everything but answers poorly is worse than one that covers key sources well.

Ignoring document quality. If your SharePoint is full of outdated, duplicate, or poorly written documents, your AI search will reflect that. Consider a document cleanup effort alongside the search implementation.

Skipping security trimming. It's tempting to launch without access controls and "add them later." Don't. A security incident in the first month will kill trust and potentially the entire project.

Not measuring baseline. Before launching AI search, measure how long people currently spend finding information. You need this baseline to demonstrate ROI.

Over-relying on generation. Sometimes users want the document itself, not a generated answer. Always provide links to source documents alongside generated answers.

Getting Started

Start small, prove value, expand. Here's a practical path:

  1. Pick one high-value source - Your most-used SharePoint library, your product knowledge base, or your policy documents.
  2. Build the index - Extract, chunk, embed, and index the content.
  3. Build a simple search interface - A web page with a search box and generated answers.
  4. Test with a pilot group - 10-20 users who currently struggle to find information.
  5. Measure and iterate - Track query quality, gather feedback, improve.
  6. Expand - Add more data sources, more users, more features.

If you're ready to build AI-powered search for your organisation, talk to our team. We design and build enterprise search solutions for Australian businesses using Azure AI Search and Azure OpenAI. Explore our AI development services and AI consulting approach to learn more about how we deliver these projects.