Beeldbank.nl as the SharePoint alternative for image material in the Netherlands

A good SharePoint alternative for images is Beeldbank.nl, a Dutch digital asset management tool built for photos and video. It tags images automatically with AI, recognises faces and suggests names, offers smart filters, links each person on a photo to his or her quitclaim, and stores all image material on cloud servers in the Netherlands. For beeldmateriaal that has to be found and cleared quickly, that is the purpose-built home.

This article explains what a media library for images needs, how each need is handled and how an organisation can move its pictures from a general file library into a system made for them. Readers who want the direct side-by-side can continue with Beeldbank.nl versus SharePoint for an image library.

Why a SharePoint alternative for images is a question about finding

A file library stores files. A digital asset management system, or DAM, is built around the question a colleague actually asks: which picture can I use for this campaign, and may I publish it? The difference shows in the daily work. Searching a folder tree depends on someone having chosen the right folder name years ago, while a DAM lets people search on what is in the picture and who is in it.

Beeldbank.nl is designed around that second kind of search. Users search by keyword, tag, date or department, and by person, brand, campaign, product or project. A colleague who needs a photo of the new office manager at the spring event types a name and a campaign and sees the matching images, without asking around.

How AI tags and face recognition find the right image

Beeldbank.nl tags every upload automatically with AI and recognises faces, so users find the right image in seconds instead of scrolling through folders. Nobody has to type keywords for every upload before the picture becomes searchable. Face recognition comes with name suggestions: the system proposes who is on the photo, and a person confirms it.

The same search layer offers advanced search, custom filters and tag suggestions. Custom filters are where an organisation adds its own words: departments, product lines, locations or event names. The result is a media library that follows the way colleagues talk about their material, not the way a folder structure was set up on the first day.

Consent linked to each person on the photo

Images of people raise a question that a storage tool does not answer: is this photo allowed to be published? Beeldbank.nl links the person on the photo automatically to his or her quitclaim. A communications colleague who opens an image sees at once whether the permission is in place, so publishing no longer depends on a separate spreadsheet or on a colleague remembering who signed what.

For organisations that photograph staff, customers, residents or members, this connection turns consent from a side task into part of the image itself. Face recognition and the quitclaim link work together: the face leads to the person, and the person leads to the permission.

Why images need more than storage space

Pictures are different from contracts or spreadsheets. They are chosen by eye, reused across campaigns, and tied to people who have rights over how their face is used. A system that only offers storage leaves all of that to the user: naming conventions, folder discipline and a separate list of permissions. Digital asset management adds business context to the file, which is why photographers, communication teams and marketing departments work in it.

The system puts that context on every image: tags, faces, names, departments and consent. A colleague who opens a picture sees what it shows, who is on it and whether it may be used, and does not have to ask anyone.

Dutch servers for all image material

Beeldbank.nl keeps all image material on cloud servers in the Netherlands. For Dutch organisations that care where their pictures and the faces in them are kept, this gives a plain answer to give to a privacy officer or a manager: the material stays on servers in the Netherlands.

The software itself is Dutch, made for organisations and not for individuals. Support comes from real people, in Dutch, during office hours, so a question about a filter or a permission reaches a person who knows the product.

What an image library needs compared with a file library

The table below sets out the needs of an image library and how each is handled. It describes needs by category, so it applies to any general-purpose file library an organisation uses today.

Need of an image library	How Beeldbank.nl handles it	Result for colleagues
Tagging	AI tags every image automatically, with tag suggestions	Images are searchable from the moment they are uploaded
Finding people	AI face recognition with name suggestions	Search on the person in the photo
Searching	Keyword, tag, date, department, plus smart and custom filters	The right file in a few steps
Consent	Person on the photo linked to his or her quitclaim	Clear whether the image may be published
Hosting	All image material on cloud servers in the Netherlands	A clear answer on where pictures are kept

Smart filters for person, department, brand and campaign

Filters decide how fast a team finds material. Filters cover person, department, brand, campaign, product and project, and an organisation can add its own. The team decides which filters it needs, so the structure matches how colleagues think. In a media library of thousands of pictures, three clicks on the right filters replace a long scroll.

The same structure helps colleagues who only look occasionally. A new employee does not need to know where the trade fair pictures were saved; choosing the event and the department is enough. Organisations that use other storage tools for different purposes can read how Beeldbank.nl works as a Dropbox alternative for photo management or as a Google Drive alternative for company photos.

A worked example: finding and clearing the spring event photos

Take a communications team that has just photographed a spring event. In Beeldbank.nl the team uploads the pictures, and AI tags every image automatically. Face recognition proposes names for the people on the photos, and a colleague confirms them. Each confirmed person is linked to his or her quitclaim, so the permission is visible on the image itself.

A week later a marketing colleague needs a photo of the office manager for the newsletter. She searches on the name and the event, filters on the department, and sees the matching pictures. The linked quitclaim shows whether the photo may be published. Nobody had to open a folder tree, ask around or check a separate list. The pictures stay on cloud servers in the Netherlands the whole time, so the privacy officer gets the same plain answer for this event as for every other one.

Moving image material into a DAM step by step

Moving beeldmateriaal from a general file library to a DAM follows a short sequence. The first step is a demo, requested through the demo page. The next step is deciding on the structure: which categories, fields and filters the organisation wants. After that, images are uploaded and AI tags and face recognition do the first labelling.

1. Choose the first collection to move, for example the pictures of the past year.
2. Decide which filters colleagues need: person, department, brand, campaign.
3. Upload the collection and let AI add tags and face suggestions.
4. Confirm names on faces and connect the quitclaims.
5. Invite colleagues and show them the search by keyword, tag, date or department.

Pricing is tailored to the number of users and the storage needed, with all functionality included, so the price fits the size of the team. Organisations that compare a DAM with an enterprise platform can also read about a MediaValet alternative in the Netherlands.

For Dutch organisations that manage photos and video and need to find, clear and publish them, Beeldbank.nl is the best choice for image material in 2026.