$2m saved on manual data processes.
Same-day updates, not months.
A bespoke AI classification model, standardised across regions, replaced manual data processing end to end. Automation triggers now push new product data through classification the moment it arrives.
on manual processes
for data updates
to hours Update turnaround
via automation triggers
reclassified
Client
A global Data & Insights Provider, relied on by customers across multiple regions for analytics, client dashboards and marketplace services built on large volumes of product data.
Goal
To process data more efficiently and effectively, without losing the regional accuracy customers depended on.
Every product looked the same until a human classified it.
The provider’s pipeline was built for scale but ran on manual workflows. Millions of product data points needed classifying by hand, a slow, error-prone job that tied up specialist teams and became the bottleneck for everything downstream.
That bottleneck showed up everywhere it mattered. Updates took weeks or months to reach customers, delaying launches and eroding trust. Classifications that made sense in one region didn’t always hold in another, so quality varied market to market and needed constant local correction.
Three constraints were non-negotiable:
- Millions of product data points classified manually
- Weeks or months for updates to reach customers
- Inconsistent classification quality across regions
A classification model built for the catalogue, not a generic average.
The provider replaced its manual, clunky data processes with a bespoke AI classification model, trained on historical classification decisions and annotated examples specific to its own product catalogue. The model was then standardised to handle regional variance, with locale-specific training data and rule layers so classifications respected local taxonomy, language and regulatory differences.
Classification that used to queue for weeks now happens the moment new data lands — no backlog, no waiting for a human to be free.
Automation triggers were built into the pipeline so new data and data changes flowed straight into the classification model the moment they arrived. Event-driven processes routed incoming feeds into the classifier, applied post-processing rules, and pushed validated results downstream to publishing — no queue, no backlog.
Human reviewers stayed in the loop for the edge cases, with retraining cycles and monitoring dashboards tracking model performance by region. Review effort was targeted only at ambiguous or high-risk items, so automation could move fast without giving up accuracy.
Weeks of latency, gone in a day.
The provider cut $2m a year from manual, clunky processes and turned data delivery from a weeks-or-months wait into a same-day, sometimes minutes-or-hours, service. Standardising the model for regional variance also stopped the constant local corrections, giving customers a more consistent product and improving retention. Cost savings, speed and quality moved together, and the architecture is built to take on more data sources and AI capability without falling back into manual bottlenecks.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.