by Francesca Brzoskowski

Imagine: a colleague in operations wants to know how your company resolved a customs delay in Germany last year. They know someone dealt with it and that emails were sent around. But as soon as they type ‘Germany customs 2024’ into the shared folder, 47 emails and 30 PDF reports appear, most of which have nothing to do with their 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 happens in almost every organisation. Not because people work messily, but because the resources to search effectively are missing.

Traditional search only works to a limited extent

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

If you type ‘onboarding problems’, you will only get documents that contain those exact words. One typo or a missing word, and important results will disappear from view.

Slimmer searching with semantic and vector search

New search techniques work differently. Whether you use a modern search engine or an MCP server linked 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 question about onboarding issues can also refer to documents about customer implementation, setup challenges, or delays with new accounts.

An MCP server goes a 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 production company had fifteen years of maintenance reports, all saved as PDFs. Technicians knew that certain machines had recurring problems, but finding similar incidents normally meant: sifting through hundreds of reports.

Following the introduction of semantic search with Elasticsearch, a technician could simply ask: “What usually causes overheating in hydraulic systems?”

Within six seconds, he received six relevant incidents back, spread across different years, locations, and formats, but thematically related. Manual searching would take hours, if anyone would even start.

When do you opt for which approach?

Which approach works best varies by organisation. These are the main considerations.

Semantic search engine (e.g. Elasticsearch)
If you’re building your own semantic search engine, the data must first be prepared: splitting documents, embedding them, vectorising them, designing indexes and testing them with real search queries. It takes work, but it delivers predictable and easily verifiable search results. Ideal if you want concise, accurate answers.

MCP servers
MCP servers allow you to skip that preparation. They work directly with what you already have: emails, tools, documents, shared folders. In return, you must ensure good authentication, permissions, and monitoring. MCP servers are quicker to implement, but harder to control without clear operational agreements.

The way forward for organisations

Technology is no longer the limiting factor. Embedding models are accessible, and MCP servers are rapidly maturing. What counts most is where your organisation 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 is mainly found in structured repositories, compliance folders, or technical documentation – and you have the time to prepare that data properly – then you can build a clean, reliable search solution that requires little maintenance.

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

In practice, many organisations opt for a combination. You can already see it 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 organisations make the right choices between semantic search, Search AI and MCP solutions. We ensure a clear approach and robust implementation, and prevent good ideas from falling by the wayside because systems are not set up or managed properly.

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

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