What exactly is automatic tagging?

Automatic tagging means that an AI reads every incoming document and suggests attributes for it: document type, customer or supplier, project, installation, date, topic. Apart from the document type, the system enters these values itself; the document type is confirmed by a person in the review area, because it determines who may see the document. This makes the document findable without anyone selecting a folder or assigning a file name according to a naming scheme.

How reliably does the AI assign documents correctly?

Clear-cut documents – a quote with a clear sender and project reference – are dealt with quickly in the review area: the field values are already filled in, and only the document type remains to be confirmed. The more ambiguous a document is, the more queries the review area raises, because it shows exactly what could not be clarified beyond doubt from the document. This keeps filing reliable, and the effort is limited to a brief approval instead of sorting work.

Can I specify my own categories and tags?

Yes – in fact, that is the recommended approach. The company decides which document types, projects and topics exist – the AI works within this framework and takes its suggestion for the document type exclusively from your list. This prevents a chaos of freely invented tags, and the terms match the language used in your company.

What happens if a document has been assigned incorrectly?

The assignment can be corrected by hand at any time, and good systems learn from such corrections for future cases. What matters is this: an incorrectly tagged document is not lost, because the search still finds it by its content – unlike a file that sits in the wrong folder.

Do I still need a folder structure at all?

Not for the knowledge base itself – there, attributes and search replace thinking in folders. Existing filing systems such as a file server or SharePoint can, however, continue to run unchanged; tagging adds an extra layer of order on top, without anyone having to rebuild the old structures.

Automatic tagging: an end to manual filing

Automatic tagging means: an AI reads every incoming document and classifies it by itself – it enters customer, project, installation, date and topic directly. Only the document type is presented as a suggestion, and a person approves it, because the document type determines who gets to see the document. This replaces the step on which orderly filing fails in practice: manually sorting documents into folders and naming files according to a scheme. The company sets the framework, the AI fills it in, and a person confirms – quickly, for clear-cut documents.

In brief

Automatic tagging classifies documents based on their content – nobody has to choose folders, assign file names or memorise filing rules any more.
From the sender, the content and the company’s known master data, the AI works out who a document comes from and what it belongs to – it enters customer, project, installation, date and topic directly.
The company specifies the categories; the AI works within this framework – so the order stays consistent rather than creative.
The document type is always confirmed by a person, because visibility depends on it; beyond that, the review area only asks about what remains open – which is why clear-cut documents go through with a single click.
Records such as invoices remain led by the ERP and accounting – tagging mainly organises knowledge: reports, manuals, correspondence, project documents.

Why does manual filing fail?

Every company has at some point agreed on filing rules: which folder, which naming scheme, which sub-structure. And in almost every company, three years later the folders still look grown rather than planned. The reason is always the same: manual filing is an extra step with no immediate benefit of its own, and steps like that are the first to be dropped under time pressure. On top of that, everyone classifies things differently – what one person files under ‘Maintenance’ another files under the customer’s name.

The result is not just disorder but mistrust: anyone who does not trust the filing system makes their own copy to be on the safe side or keeps documents in their mailbox. This makes the order fall apart even further. The way out is not stricter rules – they fail on the same principle – but reversing the work step: the system does the groundwork, and the person only confirms what they need to decide.

Which attributes does the AI recognise in a document?

Automatic tagging reads the content of the document – not just the file name – and derives from it the attributes that make it findable. It enters all but one of them itself – for the document type, it stays at a suggestion:

Attribute	How the AI recognises it	What it is used for
Document type (suggestion, approved by a person)	Structure and typical terms: a test report looks different from a quote	Filtering by type: ‘all maintenance reports’ – and controlling who may see them
Customer / supplier	Sender, letterhead, names mentioned, matching with master data	Everything about one business partner at a glance
Project / matter	Project names, job numbers, addresses in the text	Assignment to the right site or installation
Date	Document date taken from the content, not just the scan date	Placing it in time: ‘the report from January 2023’
Topic / content	The summary of the document’s content	Questions in everyday language, even in different words from those in the text

Matching with the company’s master data is important: because the system knows the customers, suppliers and ongoing projects, it assigns a delivery note from the Berger company to the Berger matter – and does not invent a new category ‘Berger GmbH & Co’ just because the letterhead looks different for once.

How reliable is automatic assignment?

The honest answer has two parts. The AI handles clear-cut documents – a quote with a clear letterhead, a report with a job number – very reliably; that is the majority of daily incoming documents. But there are always ambiguous cases: a letter with no clear project reference, a new supplier who is not yet in any master data, a batch scan with three documents in one file.

What matters is how a system deals with this uncertainty. A good system does not guess, it asks: the review area shows exactly what could not be clarified beyond doubt from the document, and it is confirmed or corrected with two clicks. One query is always pending: approval of the document type – it determines who gets to see the document later, and that decision stays with a person. For a clear-cut quote, that is one click; for a batch scan without a sender, it will be a few more. The system learns from the corrections for the future. This reverses the relationship – instead of sorting one hundred per cent of documents by hand, you confirm groundwork that has already been done. And even an incorrect assignment is no disaster: unlike a file in the wrong folder, an incorrectly tagged document can always be found via the content search.

Who decides the categories – the AI or the company?

The company. This is not a minor detail but the prerequisite for the order to suit the business. During setup, it is decided which document types exist, which projects and topics are relevant and which terms the company uses – a plumbing and heating installer thinks in terms of installations and maintenance contracts, a tax firm in terms of clients and deadlines. The AI then moves within this framework instead of associating freely – the document type it presents for approval also comes from this list.

This framework also does away with the old worry about ‘tag chaos’, where every employee invents their own terms and in the end ‘vehicles’, ‘cars’ and ‘fleet’ exist side by side. Precisely this inconsistency, on which manual tagging has always failed in practice, disappears when all suggestions come from a single set of rules. Maintenance remains manageable: new projects and new document types are added when they arise – a matter of minutes, not an ongoing task.

What does this mean for invoices, delivery notes and other records?

An honest classification is part of this: for records, the leading system is and remains accounting or the ERP. That is where invoices are checked, posted and retained in accordance with statutory periods – a knowledge base does not replace this and is not meant to. The ERP knows what happened: which order, which invoice, which payment status.

The knowledge base adds to this where a record needs context – and above all where no record exists at all: in the how. The setting report for the installation, the correspondence about the complaint, the manual written by an experienced employee, the photo of the type plate. These documents never had a leading system – they were scattered across folders and mailboxes. For them, automatic tagging is the step that turns scattered files into orderly, findable company knowledge.

How do you introduce automatic tagging?

In practice, tagging is not a separate project but the engine behind document capture as a whole: whatever comes in from email mailboxes, from the scanner, from mobile phones or from network folders automatically goes through text recognition (OCR) and tagging and is then ready for approval. It is therefore set up together with the connection of the first sources – which collection comes first and the order in which connecting them has proven its worth is shown in the overview Connecting documents and systems.

In Goma-it’s AI knowledge base, automatic tagging is at the core of the system: the framework is set up together with you, the system sets the field values itself, and you approve the document type in the review area – so you stay in control of who sees which documents. In addition, every AI answer names its source document, and your documents stay on EU servers without being used to train third-party AI models. The implementation process page describes how the introduction works step by step – from defining the categories to day-to-day operation.