by Francesca Brzoskowski

Imagine this: a colleague in operations wants to know how your company resolved a customs delay in Germany last year. She knows someone has picked it up and that emails were circulating about it. But as soon as she types "Germany customs 2024" in the shared folder, 47 emails and 30 PDF reports appear, most of which have nothing to do with her question.

She asks three colleagues for help. One sends a report, another adds someone from logistics. An hour later, she has eleven browser tabs open, but still no clear answer.

This is a problem in almost every organization. Not because people are disorganized, but because they lack the resources to search effectively.

Traditional search works only to a limited extent

Many organizations still rely on traditional search methods. These primarily search for exact word combinations: in emails, files, folders, or subfolders.

If you type "onboarding issues," you'll only see documents containing exactly those words. A typo or a missing word will cause important results to disappear.

Search smarter with semantic and vector search

New search technologies work differently. Whether you're using a modern search engine or an MCP server connected to your apps, these systems understand the meaning behind words.

Text is converted into vectors, or numbers that represent the meaning of words. This allows a search engine to understand that a query about onboarding issues could also refer to documents about customer implementation, setup challenges, or delays with new accounts.

An MCP server goes one step further. It searches all your data simultaneously—emails, documents, PDFs, shared drives—and automatically determines where and how to search.

A practical example: from hours to seconds

A manufacturing company had fifteen years of maintenance reports, all saved as PDFs. Technicians knew that certain machines had recurring problems, but finding similar incidents usually meant reading through hundreds of reports.

After implementing semantic search with Elasticsearch, an engineer could simply ask, “What typically causes heating in hydraulic systems?”

Within six seconds, he received six relevant incidents, spanning different years, locations, and formats, but all related in content. Manual searching would have taken hours, if anyone even bothered to do it.

When do you choose which approach?

The best approach varies from organization to organization. Here are the key considerations.

Semantic search engine (e.g. Elasticsearch)
When building your own semantic search engine, the data must first be prepared: slicing, embedding, vectorizing, designing indexes, and testing them with real queries. It's a lot of work, but it delivers predictable and easily verifiable search results. Ideal if you want concise, precise answers.

MCP servers
With MCP servers, you can skip that preparation. They work directly with what you already have: emails, tools, documents, shared folders. However, you do need to ensure proper authentication, permissions, and monitoring. MCP servers are faster to implement, but more difficult to manage without clear operational agreements.

The way forward for organizations

Technology is no longer the limiting factor. Embedding models are accessible, and MCP servers are maturing rapidly. What matters most is where your organization stands:

• How structured is your data?
• How do you want to build search functionality?
• Who will use it?
• How much control do you need over the quality of results?

If crucial knowledge resides primarily in structured repositories, compliance folders, or technical documentation—and you have the time to properly prepare that data—you can build a clean, reliable, low-maintenance search solution.

But is information fragmented across tools, inboxes, and shared drives? And do you need answers that search everything simultaneously? Then an MCP server helps you work with today's reality.

In practice, many organizations opt for a combination. You already see this in copilots, Apple's MCP integration in Siri, and new servers that combine data across multiple platforms. Not to build perfect technology, but to make information accessible so people can do their work without endless searching.

How PuurData helps with this

At PuurData, we help organizations make the right choices between semantic search, Search AI, and MCP solutions. We ensure a clear approach, robust implementation, and prevent good ideas from failing due to improperly configured or managed systems.

If your teams are still spending hours searching for answers that should take seconds, it's time to think differently.

Let's explore together what works best for you. Request a consultation with one of our experts.