Enterprise knowledge base showcase

AI Knowledge Assistant

Critical knowledge scattered across SharePoint, PDFs, and people's heads. An enterprise AI assistant that understands documents semantically, answers questions with source citations, and works inside the tools the team already uses.

Built on PowerApps, Copilot Studio, and Azure AI Search. The assistant ingests documents from SharePoint and file shares, indexes them semantically, and provides contextual answers with citations — accessible via a PowerApps canvas app or embedded Teams widget.

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Search time

-70%

Average time to find a policy or procedure

Documents indexed

1,000+

SharePoint, PDFs, Word docs in one index

Answer accuracy

Cited

Every answer links back to source documents

Business framing

Why this mattered

The organisation had years of accumulated knowledge spread across SharePoint sites, shared drives, and the institutional memory of long-tenured employees. New staff took weeks to get up to speed. Finding the right policy document required knowing the right person to ask. The solution was not another document management layer — it was an assistant that could actually read and reason about the documents that already existed.

Observed pain

  • Onboarding time was dominated by knowledge discovery, not actual learning.
  • Compliance queries required expert staff to answer manually — a bottleneck that didn't scale.
  • SharePoint full-text search returned too many results without context or ranking by relevance.

Guided walkthrough

Each block shows the business reason, the system move, and the operational implication.

Slide 01

One search interface across all documents

Azure AI Search indexes documents semantically, not just by keyword. A question about "refund policy for damaged goods" finds the right section of the right document even if those exact words never appear together. The search layer handles multi-format ingestion: PDFs, Word docs, and SharePoint pages are all treated as the same corpus.

  • Semantic search — intent-based, not keyword-matching
  • Multi-format: PDF, Word, SharePoint, HTML
  • Relevance ranking surfaces the most applicable source first

Slide 02

Answers with citations, not walls of results

Copilot Studio connects Azure AI Search to a generation layer. Instead of returning a list of document links, the assistant synthesises a direct answer from the retrieved content and attaches the source references. Staff get the answer they need and can drill into the source if they want to verify it.

  • Contextual answer generated from top-ranked document chunks
  • Source citations embedded in every response
  • Follow-up questions handled within the same conversation

Slide 03

Accessible where the team already works

The assistant is surfaced through a PowerApps canvas app and optionally embedded in Microsoft Teams. No new login, no new system to learn. Permissions from SharePoint carry through — users only see answers derived from documents they already have access to.

  • PowerApps front-end — works on desktop and mobile
  • Teams channel bot for quick queries without leaving chat
  • SharePoint-level permissions respected throughout

Workflow anatomy

Each stage is small enough to inspect, yet together they form a coherent system.

01

01 · Ingest

Documents indexed into Azure AI Search

A scheduled indexer crawls connected SharePoint libraries and document sources. New or updated files are chunked, embedded, and added to the search index. The index stays current without manual intervention.

02

02 · Retrieve

Semantic search finds the relevant passages

The user's query is embedded and compared against the document index. The top matching passages are retrieved along with their source document metadata, ready to be passed to the generation layer.

03

03 · Generate

Copilot Studio synthesises the answer

The retrieved passages are sent to the Copilot Studio knowledge base topic. The AI model generates a natural language answer grounded in the retrieved content, citing which document each part of the answer came from.

04

04 · Surface

Answer delivered in PowerApps or Teams

The response is rendered in the PowerApps interface or Teams message with clickable source links. Follow-up questions continue the conversation context. The session is logged for continuous improvement.

Business impact

What changed for operations

  • New staff find answers independently in minutes instead of asking colleagues — onboarding friction reduced significantly.
  • Compliance and policy queries handled by the assistant instead of routing to specialist staff for routine lookups.
  • Knowledge that lived in specific people's heads becomes accessible to the whole organisation through the document index.

Architecture note

Routing logic in plain English

  • Document sources → Azure AI Search indexer → semantic index → Copilot Studio RAG pipeline → cited answer → PowerApps or Teams surface
  • Permissions from SharePoint propagate through the index — users never receive answers from documents they cannot access.
  • The generation layer is grounded in retrieved content only; the assistant does not speculate beyond what the indexed documents contain.

Stack in play

Use what the business already has, then make it behave like a coherent system instead of a collection of tabs.

Azure AI Search

Hosts the semantic document index. Handles chunking, embedding, and relevance-ranked retrieval across all connected document sources.

Copilot Studio

Orchestrates the retrieval-augmented generation (RAG) pipeline: receives the query, calls the search index, and generates the cited answer.

SharePoint / OneDrive

Primary document sources. The indexer crawls connected libraries on a schedule. Permission boundaries from SharePoint are preserved in the search layer.

PowerApps canvas app

The user-facing interface. Provides a conversational search experience accessible on desktop and mobile without requiring a separate login.

Microsoft Teams integration

Optional bot channel that embeds the assistant directly in Teams, allowing quick queries without leaving the communication platform.

Reusable pattern

Enterprise Microsoft stack

This showcase demonstrates the practical Microsoft-native path to enterprise RAG: no custom vector database, no bespoke Python pipeline — just Azure AI Search, Copilot Studio, and the Microsoft 365 tools the organisation already pays for.