Custom models for procurement spend classification

One supplier, different purchases

Take an illustrative manufacturing company. The same distributor supplies it with protective gloves and machine spare parts. A rule that assigns all spend with the distributor to safety equipment gives a technically consistent report, and it hides maintenance spending. When the team plans a joint purchase of gloves with other plants, it works from the wrong basis.

A model that reads individual line items could separate these purchases, if it gets information that lets it recognise them. The description “protective gloves” tells you something different from “materials as per order”. In the second case a link to the purchase order may be needed. Using a more complex model will not fill in the missing context reliably.

In a pilot, assess line items with a clear description separately from those that need extra documents. If the improvement depends mainly on access to orders, connecting the data will be a key part of the project. Buying training alone will leave that problem unsolved.

Who decides which spend category an expense belongs to

A procurement taxonomy is an ordered split of spend into categories and subcategories. It should match the decisions the purchasing team takes. An accounting account describes a different dimension of the records; do not assume its code is enough to set the category used in negotiations. A project can use both pieces of information while keeping their meanings separate.

Before choosing technology, name the person responsible for the split and for settling disputes. In the distributor example, purchasing and maintenance have to agree where spare parts go. If each plant uses the same name for a different scope, a shared report will not be comparable even after AI is rolled out.

Before classifying spend, you need agreements with the report owners on the category split and the quality of the training data. For the buyer, that means checking whether the history of assignments reflects the current split. Good results at reproducing old categories may be of little value when the company is changing them.

What to compare before ordering your own model

Rules assigned to suppliers are worth checking where the company buys a clearly defined range from them. They are easy to explain to the team. For a distributor serving several areas, though, they need extra information about the line item.

If your current purchasing system has a spend classification feature, include it in the comparison. Check the category split it supports and whether its results can feed your current reports. Your own app has to justify the work needed to maintain it and connect it to your systems.

A general model can be assessed on understanding descriptions and assigning them to the agreed categories. When recurring errors specific to the company's range remain, a custom classifier or fine-tuning, adapting an existing model through further training, becomes a candidate for further testing. Fine-tuning is no mandatory stage and no guarantee of a better result.

The comparison should also cover handling exceptions. A solution that recognises frequent purchases correctly, but hands most harder line items to analysts, may still need a lot of work. Know that effort before you decide on a rollout.

How to accept the pilot result

Ask for the methods to be compared on the same, previously unseen line items. They should include both known suppliers and purchases from companies the model did not meet during adaptation. Such a test helps you tell recognising the type of purchase apart from relying on a familiar supplier name.

The overall result has to be broken down by category and by the effect of errors. Confusing two subcategories of gloves can matter differently from counting machine parts as safety equipment. Also assess the value of wrongly assigned spend: a few large line items can change the picture of a category even when many small purchases are classified correctly.

Agree what the system does with a description that does not allow a category to be chosen. You need a person who checks such line items and a way to pass them the context. Purchasing systems with spend classification often show the confidence of an assignment and allow manual correction, which is a reference point when you assess the team's workload. For your own project, check whether the confidence indicators help catch errors on the company's data.

Also set the point at which a result may feed the report. A proposed category waiting for review and an accepted assignment should be distinguishable to the reader of the analysis. The pilot scope can include a comparison report, a review of errors and a recommendation for next steps with its limits.

The scope of the rollout and ongoing responsibility

New suppliers and changes to the range can change the kind of descriptions that reach classification. A separate event is a rebuild of the categories, for example splitting spare parts out of the wider maintenance area. In both cases the results have to be assessed again, and only the second changes the definition of a correct answer. The offer should state who reports such changes and how the scope of updates is set.

Syntalith builds AI apps and solutions with custom models, as well as automations and custom software. For spend classification, this lets us consider the model, access to purchasing data and the handling of corrections together. What remains to agree is which parts the pilot covers and which a separate rollout does. We price a private model after a free call about the task, the data and the hardware. An app that uses the model starts from €3,000 net (pricing).

In your enquiry, describe the report you do not trust today and the purchasing decision that depends on it. Name the system you use, the information available about line items and the person responsible for the categories. Also prepare examples of errors and how they are checked today. From that you can settle whether the first order should be a comparison of classifiers, tidying the categories or access to the missing data.

What a spend classification model costs and what it saves

Where to start

In the free process review we choose one process and 20 to 100 cases the team has already handled. We compare the simpler change with the adapted model on cases the model has not seen, and within 2 working days you get a written summary with a price. We adapt models as part of private LLMs, and build the app as an AI app.