Semantic search with Copilot: a complete guide and real-world examples

Semantic search with Copilot has gone from being a promise to becoming the new standard for information discovery in environments Microsoft 365 and Power BI. It combines classic lexical cues with vectorial understanding of meaning. to locate relevant content even if we don't remember the exact file name, email subject, or precise column label.

Beyond the traditional search engine, this proposal connects with Microsoft Graph and a next-generation semantic index. The result is a much more contextual, personalized, and secure information retrieval, aligned with user permissions, relationships between people and documents, and the actual intention expressed in natural language.

What is semantic search with Copilot and why does it matter?

In the Microsoft 365 world, data indexing has been facilitating access to information for years, but now it takes a qualitative leap with the semantic index. This index complements the classic lexical index with vector representations (vectors) of documents and terms, which allows measuring similarities by meaning and not just by exact word matching.

Based on this, Copilot understands queries like “document from the supplier congratulated on its design” even if the word “congratulated” does not appear literally, because Expand your search with synonyms and similar forms. (praised, commended, excellent…) and leverages the context of Microsoft Graph to prioritize what is most relevant.

The magic happens inside your tenant: Copilot respects the boundaries of safety, compliance, and privacy.It uses role-based access control and doesn't display anything the user isn't authorized to access. All this without requiring any extra effort from you: Microsoft automatically enables the semantic index at the tenant level and, progressively, at the user level.

Lexical and semantic index: how they combine

The lexical index remains useful for searches by keywords, specific names, and predefined filters. The semantic index provides conceptual understandingIt creates vector spaces where documents are "grouped" by meaning. In this way, vague questions find accurate answers.

In practice, Microsoft 365 Copilot uses both approaches: it consults the Microsoft Graph, gathers relevant evidence from your organization, enriches the LLM prompt (language model) and returns a more substantiated result. Throughout the entire process, encryption in transit (HTTPS) and at rest is maintained, in addition to Microsoft's privacy policies.

What does the user gain? Consistent, contextual, and personalized results that reflect social signals (who you work with, what you open) and relationships between content, without you having to memorize internal nomenclatures or literal accuracy.

Data flow: from your query to Copilot's response

When you write a request in a Microsoft 365 app, the request goes to Copilot. Copilot consults Microsoft Graph and the semantic index to prepare a prompt The enriched data is then more accurately understood by the LLM. The system then returns to Graph to refine the output and finally presents the text and actions in the corresponding application.

This back-and-forth guarantees two things: grounding and strict compliance with permissions. Nothing is "invented" outside the user's control, and the data always remains within your tenant and their authorized regions.

With ThereMicrosoft is expanding the granularity: there is already a tenant-level semantic index powered primarily by textual content from SharePoint Online and, progressively, user-level indexes are built that prioritize the daily “work set” (emails, documents you participate in, mentions, etc.).

Content types, index updates, and administration

Today, the semantic index includes user mailboxes (in the personal sphere), and documents from Word, Power point, PDF, aspx pages and OneNote, among others, for the tenant. Graph connector data is indexed at the tenant levelso the scope increases with content from external systems, always respecting permissions.

Updates are quick: What you create in your personal mailbox is indexed almost in real time.SharePoint documents accessible to two or more users are refreshed daily, and changes to already indexed documents are reflected quickly. Administration requires no activation, although you can fine-tune certain settings.

For example, if you don't have Microsoft Purview DLP and need to exclude highly sensitive content (payroll, finance, HR) from SharePoint Online, You can mark those sites so they don't appear in results. from Microsoft Search or the semantic index. You can also manage "insights" for people and items, knowing that disabling them will cause the user to lose useful relevance signals.

Security, compliance, privacy, and storage

Copilot inherits the controls from Microsoft 365: business security, regulatory compliance and privacy (including GDPR and EU Data Boundary). Queries and results do not train the underlying models. BYOK (bring your own key) is supported if it is already enabled in your environment.

Where does the index data go? The user index is hosted where your mailbox resides.The tenant index resides in an isolated container within the SharePoint site region. Customers within the EU data boundary store their index in EU/EFTA data centers, while processing respects multi-geography and configured regional policies.

All of this does not change one key reality: Indexing does not alter permissions or quotas. storageIt only works with content that you already have access to, applying the same identity and role control model as the rest of Microsoft 365 services.

Copilot connectors and third-party data

With the right connectors, you can bring in SaaS content, databases and repositories external to Microsoft Graph. Once inside, that content is also indexed and it remains available to Copilot with the same access controls. It's important to enrich it with text to maximize semantic relevance.

In enterprise licensing, you will find specific requirements depending on the connector and the Copilot product. This expansion turns Copilot Search into a single access point Microsoft and third-party information, with hundreds of integrable Microsoft and ISV connectors.

Copilot search in Microsoft 365: experience, differences, and answers

Copilot Search appears as a "search" module within the Microsoft 365 Copilot app for web, desktop, and mobile. Their proposal: a fast, relevant, and universal experience which connects to chat to delve deeper or perform subsequent tasks.

It accepts queries in natural language (“show me emails from Marta about the Q4 forecast sent last week”) and also traditional keywords. When the query allows it, it displays concise "Copilot responses". at the top, based on Graph and, where appropriate, on enabled web and connected services.

How is it different from Copilot chat? The search is designed for quickly find what you needWhile chat focuses on generating content, explaining, transforming, and linking actions, compared to Microsoft Search's "free" keyword-based search engine, Copilot Search (paid) adds semantics, unification, and a modern experience, in addition to deep integration with Copilot Chat.

Search API: Hybrid search (lexical + semantic) in OneDrive

For developers, the Copilot Search API exposes the capabilities needed to View professional/educational content on OneDrive Using natural language, without replicating or reindexing data outside of Microsoft 365. Natural language processing understands intent and ranks results by relevance.

Advantages of customized solutions: semantic relevance, data freshness, lower cost of ownership and respect for original permissions. You can filter by specific paths using KQL and get more relevant previews, metadata, and files without setting up parallel search infrastructure.

Best practices: write descriptive queries, add context, avoid overly generic terms and Use the full OneDrive path when filtering by path (for example: https://contoso-my.sharepoint.com/personal/usuario_empresa_com/Documents/Proyecto/Informe.docxEverything is automatically sorted by relevance.

Current limitations to consider: OneDrive only for work or school1500 characters maximum per query, 200 requests per user per hour, limited file size for semantics (up to 512 MB in .docx/.pptx/.pdf; less in others), without semantics on pure tables/images/graphs, and availability is adjusted to what is supported by the M365 Copilot semantic index.

New in Windows: Ask Copilot in the taskbar

Microsoft is testing “Ask Copilot” with Insiders in Dev and Beta channels: a revamped shortcut that replaces the traditional search bar for a lighter and faster floating window. It shows no pre-window noise, opens quickly, and simplifies the Boot of queries.

The integration is hybrid: for local searches of apps, files and settings uses the APIs of Windows Search and the SearchIndexer servicewithout Copilot directly accessing your files. The semantic layer interprets the intent, but in early versions some natural commands open the configuration instead of executing the action.

The Vision and Voice buttons appear, although In the first builds, they redirect to the main Copilot app.This feature is optional and disabled by default; you can enable it in Settings > Personalization > Taskbar. Classic search is still accessible from the Start menu.

Copilot in Power BI: how to prepare an “AI-ready” semantic model

In Power BI, Copilot transforms the experience: generating visuals, DAX measures, summaries and explanations based on natural language. But the quality of the responses depends heavily on the semantic model: if it is not ready, ambiguities and less useful responses will appear.

An AI-ready model incorporates a data schema of IA (AI data schema), synonyms, verified responses, and “AI instructions” that guide Copilot. Furthermore, it relies on a robust data design: clear relationships, understandable names, correct data typescoherent measures and useful hierarchies.

Fabric Copilot capability has been available since April 2025. for all Microsoft Fabric capabilities (F2 and above)This reduces the cost of accessing Copilot in resource-constrained environments and facilitates its widespread adoption.

AI data schema, synonyms, and instructions

The AI ​​data schema allows prioritizing key fields (date, customer, category, amount, margin, region…) and distinguishing entities and attributesWith understandable names (“Customer”, “Sale ID”) and synonyms (“Product” ~ “article”, “reference”, “item”), Copilot better understands what users are asking for.

The “AI instructions” add global context to the model: Which metrics are prioritized, how is “active customer” defined?What calculations should be avoided to prevent mixing concepts? For example: “The 'Customer Status' field distinguishes assets if there was at least one purchase in 12 months; margin and profit measures apply only to assets.”

These definitions, along with the schema and synonyms, are stored in the dataset and sent to the service upon publishing. The effect is noticeable immediately. in the quality of the automated responses and in the choice of visuals and measures.

Proven answers and best practices in modeling

The “proven answers” ​​mark official definitions for key concepts (what is “net margin”, “active customer”, etc.). Copilot can directly return that verified answer. when it detects the related query, reducing debates and misunderstandings.

Furthermore, it is advisable to standardize measurements and nomenclature: descriptive and consistent namesClear and documented DAX logic, and a pre-prepared set of common measures. Examples: Total Sales = SUM(Sales[SaleAmount]), YTD Sales = TOTALYTD(SUM(Sales[SaleAmount]), 'Date'[Date]) o MoM Growth = DIVIDE([This Month Sales] - [Last Month Sales], [Last Month Sales]).

In the model, separate the fact tables well (FactSales, FactTransactions, FactVisits) and those of dimensions (for example, DimProduct ProductName, Category, Brand, and DimCustomer CustomerName, City, Segment), creates logical hierarchies (Date: Year > Quarter > Month > Day; Geography: Country > State > City), use correct data types (numbers as numbers, dates as dates) and correctly defines the cardinality and state of the relationships.

Don't forget the consistency of values ​​(“Open”, “Closed”, “Pending” with the same capitalization), the usual KPIs (ROI, CAC, LTV), transparency in updates (“daily at 6:00 UTC” or incremental every 15 minutes), role-based security (filters by region or by sensitive tables), and the model documentation (descriptions in tables/columns and data dictionary).

Mark the model as AI-ready and license

When the model is ready (AI scheme, synonyms, instructions and, preferably, tested responses), Mark it as IA-ready From Power BI Desktop, publish to a Premium or Fabric workspace. You'll see the indicator in the dataset within the service.

Enabling AI-ready allows Copilot to have full access to the relevant elements of the modelIt will better understand questions in natural language and improve the generation of visualizations and metrics. In the medium term, this reduces technical intervention and accelerates adoption by business users.

Please note the licensing requirements: Search API and Copilot Search require a valid Microsoft 365 Copilot license.In Power BI, the availability of Copilot depends on the capacity and configuration of the tenant/workspace.

Power BI content search with Copilot (standalone experience)

In the standalone Copilot experience, you can search for Power BI “elements” (reports, semantic models, workspace or organizational apps, and data agents). Copilot analyzes metadata and internal content from the reports (page names, visual titles, filter panels, and text boxes) to find what is most relevant.

Among the signals that are rising in the rankings are: mark as favoriteWhether it's been recently opened, approved, popular, or part of an app, adding clear descriptions, distinctive keywords, and approval tags will make your items more easily identifiable in search results.

Administrators can limit the search to AI-ready content in a workspace or at the tenant level. Copilot always respects permissions, both public app permissions and hidden elements.and does not return results that the user does not have access to. Direct changes to an item are reflected in minutes; indirect changes (such as renaming a workspace) can take up to 24 hours.

If you start with a broad question (“How many tourists visited in January?”), Copilot will suggest candidate items and ask you to confirm which one to use. You can also manually attach the exact item and chat with the data even if that element is not marked as AI-ready.

Information protection: minimization and responsible sharing

Beyond DLP and site exclusion, there are practices that help: minimize data without business value through retention/deletion (Microsoft Purview) prevents excessive sharing by inheriting correct permissions and applying real-time access checks. confidentiality labels with encryption and visual marking where appropriate, and uses DLP to temporarily limit access to documents with incidents.

Remember that indexing does not change how data is shared or add content to the index by using "organization-wide" links. Only what the user actually accesses is indexed for them. with their identity and permissions.

If you stick with one idea, let it be this: Semantic search with Copilot connects meaning, context, and safety so you can find what's important faster; and when you prepare Power BI with an AI-ready model (AI schema, synonyms, verified answers and instructions), the leap in accuracy and usefulness is noticeable in your day-to-day work, from Microsoft 365 to your critical reports and data.