Client caseRetail · South Africa

30,000 product lines classified per buying decision, in a week, for FARO

For every container purchase, FARO needed to categorise around 30,000 product lines to estimate margin, sale price, and stock time before committing capital. Pearstop automated the classification and linked it directly to FARO’s sales database.

30klines classified per buying decision
95%classified automatically
1 weekclassification time, was six months by hand
Up to 7%higher sell-through, from linking sales data to buying decisions
The situation

Why margin was invisible until after the purchase

For each container purchase, FARO needed a reliable cost picture before committing capital. That meant classifying thousands of product lines, linking them to sales outcomes, and keeping the process fast enough to support the buying decision itself.

Before Pearstop, this was two people’s job: flagging every incoming product line by hand, and mapping each one to a category by hand. Without automation, the work was repetitive, slow, and hard to scale.

This project predates general-purpose LLMs becoming viable for this kind of work. Pearstop built the classification system for FARO using machine learning and its own purpose-built technology, not a large language model — proof the underlying approach holds up without leaning on the newest tooling.

How it worked

How Pearstop estimates margin before the purchase

1

Automatic classification

Pearstop classified 95% of the items in under a week, using the company's own category logic.

2

Sales-linked margin view

The dataset was linked to sales information so buyers could see margin before the purchase happened.

3

Decision speed

The team stopped spending days on manual categorisation and got a clean basis for the buying decision itself.

We had thousands of product lines that needed to be categorised before we could even begin to understand our costs. Pearstop classified them in under a week. That would have taken our team six months and still would not have been this accurate.
David TorrDavid TorrCEO, FARO
What changed

From manual flagging to an automated flow

The team moved from manual categorisation to an automated flow that could keep up with buying decisions. That made margin visible earlier and reduced the operational drag on the procurement team.

What it changes

A more reliable basis for planning

With classification no longer the bottleneck, FARO has a more reliable basis for planning and analysis across every buying decision, not just the ones there was time to review by hand.

Want a case study built around your data?

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Send a representative sample of your invoice lines — up to 200 lines, or a handful of invoices (max 10) — and we'll classify them to show you exactly how it works. No cost, no commitment.

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