Examples

How operational AI improves pricing, stock and fulfilment

This page shows a range of work we've built to support real operational decisions. It is not a menu.

The reality is that no two organisations have the same systems, constraints, or decision pressures. What matters is not the category, but the decision behind it.

These examples are here to make our capabilities tangible. If your problem isn't shown here, that's normal.

Each example targets a measurable shift: fewer stockouts, faster pricing response or improved fulfilment accuracy.

If you'd like to explore whether we can help in your context, we should talk.

Virtual try-on (e.g. glasses)

01Planning

Understanding how people judge fit, tone, and confidence when trying products on themselves

02Blueprint

Designing face-mapping, rendering logic, and interaction flows for real-time try-on

03Execution

A live try-on experience used on a phone or desktop to support confident purchase decisions

Other use cases: Makeup · Jewellery · Accessories · Protective eyewear. Similar operational decisions.

Style & outfit matching

01Planning

Understanding how people combine items to express style, preference, and intent

02Blueprint

Defining relationships, compatibility rules, and visual groupings between products

03Execution

A matching experience showing complete looks rather than isolated items

Other use cases: Gift sets · Interiors · Event styling · Wardrobe curation. Similar operational decisions.

Compatibility / fit checkers

01Planning

Identifying where uncertainty or error occurs when selecting compatible products or parts

02Blueprint

Encoding fit rules, constraints, and validation logic into a clear decision flow

03Execution

A checker confirming compatibility before an order is placed or approved

Other use cases: Parts lookup · Tyre fitment · Filter matching · Replacement components. Similar operational decisions.

In-context product placement (e.g. furniture)

01Planning

Exploring whether a product belongs in a real space, considering scale, style, and surrounding context

02Blueprint

Designing spatial alignment, scale accuracy, and visual blending to place products naturally into real environments

03Execution

A placement experience showing products positioned convincingly within a real room or setting

Other use cases: Plants · Appliances · Outdoor furniture · Office fit-outs. Similar operational decisions.

Product visualisers (e.g. paint)

01Planning

Exploring how people imagine products in their own physical space before committing

02Blueprint

Building visual, spatial, and configuration logic to represent products accurately

03Execution

A visualiser in use, showing products applied to real environments and contexts

Other use cases: Car configurators · Flooring · Paint · Window treatments. Similar operational decisions.

Room & space planners (e.g. fence)

01Planning

Mapping how people think through layout, constraints, and trade-offs in a given space

02Blueprint

Structuring rules, dimensions, and options into a guided planning interface

03Execution

A planner being used to design and review a real room or outdoor space

Other use cases: Kitchen layouts · Garden design · Office planning · Windows & doors · Storage solutions. Similar operational decisions.

Have a different decision in mind?

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Common questions about these examples

Common questions about these examples

What do these examples represent?

These examples show how operational decisions across pricing, inventory and fulfilment can be structured and improved using applied AI.

Are these theoretical or based on real situations?

They are based on real operational scenarios where decisions already exist and have measurable commercial impact.

How transferable are these examples?

The patterns are transferable across mid-market distributors where similar decisions are made under comparable constraints.

What kind of results do these approaches deliver?

Typically improved margin, reduced stock imbalances and more consistent operational outcomes over time.

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