Enterprise Search

When Information Exists but Remains Unfindable

Your employees spend an average of 20% of their work time searching for information. The documents exist—Google Drive, SharePoint, Confluence, Slack, email—but no one knows where they are or which version is authoritative. The cost of this disorganization is rarely measured because it is diffuse: a few minutes here, a question to a colleague there, and occasionally a procedure applied from an outdated version. Enterprise Search addresses this with a unified search layer powered by AI that understands questions in natural language and queries all your internal sources.

Understanding Meaning Rather Than Words

Traditional engines, whether SharePoint search or the Google Workspace search bar, rely on keyword matching. If you search for “travel expense reimbursement procedure,” you will only find the document if it contains those exact terms. Yet internal documents are rarely named the way people ask questions about them.

Enterprise Search captures the intent of the question and matches meaning, not words: a document titled “Expense Report—Internal Policy 2026” will surface even if “reimbursement” and “travel” appear nowhere in the title. This gap between the writer’s vocabulary and the reader’s vocabulary renders most document repositories unusable, and semantic search corrects exactly that.

The output also changes in nature. Instead of a list of documents to open one by one, Enterprise Search generates a written response in natural language, accompanied by precise sources—document, page, paragraph. To the question “What is the reimbursement limit for a business dinner?”, the answer takes this form: “The limit is €60 per person for a business dinner with an external client, upon presentation of a receipt. Source: Expense Report—Internal Policy 2026, section 3.2.” Citing the source is not cosmetic: it allows the employee to verify the information and their manager to make a final decision in case of doubt.

What You Need to Know About the Architecture

The operation relies on three components. Connectors are the bridges between Enterprise Search and your data sources—Google Drive, SharePoint, Confluence, Slack, and others. The semantic index transforms documents into digital representations, or embeddings, which capture meaning rather than just words. The response engine is a Mistral model that synthesizes relevant information into a structured answer.

There is no need to go deeper into the technical details: three implications are enough for a decision-maker. Data remains within your infrastructure or in the European Mistral cloud, which answers your CISO’s first question. Indexing is automatic and incremental; a document created this morning is taken into account within minutes. And permissions from your source tools are respected: an employee cannot find via Enterprise Search a document they do not have access to in Google Drive.

State this last point unambiguously to your teams: if a document is shared only with the HR team, only its members will see it in the results. Connectors handle this mechanism without manual configuration, saving you from recreating a parallel rights repository destined to diverge from your own.

Use Cases That Deliver Results Quickly

Onboarding is the first proving ground. A new employee asks their questions directly—how to configure the VPN, where to find the job description template, how to book a room—and gets sourced answers in seconds, instead of bothering five colleagues and waiting for their availability.

Internal support follows closely. IT, HR, and legal teams devote a significant portion of their time to answering the same questions; by enabling employees to find answers themselves, Enterprise Search reduces ticket volume by 30% to 50% depending on the organization and frees these teams for cases that require their expertise.

Two more specialized use cases come next. Due diligence and document research concern legal, M&A, and compliance teams: identifying relevant passages in hundreds of documents in seconds changes the scale of work possible within a tight deadline. Knowledge management interests organizations that capitalize on their knowledge—consulting firms, engineering offices, research bodies—and want to make accumulated expertise accessible, even when experts have left the organization.

Limits to Announce Before Deployment

Presenting Enterprise Search as the solution that will answer everything guarantees disappointment six weeks later. Its performance depends on the quality of source documents—poorly structured files, those without titles, or those consisting of poor scans yield poor answers—connector coverage, as a source without an available connector is not indexed, and index freshness, with only minutes separating a document modification from its inclusion.

Treat these three limits as prerequisites rather than defects. Cleaning and structuring the document spaces you intend to index is the most profitable investment in the project, and it also benefits those who will continue to search manually.

Key Takeaways

• Enterprise Search replaces keyword search with semantic search in natural language
• Permissions from your source tools are automatically respected
• Priority use cases include onboarding, internal support, and document research
• Result quality depends directly on the quality and structure of your source documents