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Advanced AI & e-commerce

Advanced AI for e-commerce and industry

Agents, recommendations and camera-based quality control, trained on your data.AI tools that sell, answer and check. Each one is built on your company's real data and measured on results.

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In this pillar

  • Recommendation engines
  • AI suite for e-commerce
  • AI agents
  • Camera-based quality control

~23%

of revenue comes from the recommendation engine

Real result · Fashion e-commerce, Italy, 2025

The problems we solve

Do you recognise one of these situations?

The basket stays light

Customers buy one product and leave. The catalogue is wide, the suggestions are random.

Always the same questions

Availability, delivery times, order status: hours spent on requests that repeat every day.

Quality depends on who is on shift

At the end of the shift attention drops, defects get through and the customer finds them.

Solutions · Advanced AI & e-commerce

What we can build for you

Each solution can be switched on alone or together with the others.

Recommendation engines

recommendation engine, cross-selling and upselling

Suggestions built on your shop's real orders, on the website and on WhatsApp, with a dashboard to follow average order value and conversions.

Who it is for: E-commerce, online retail, brands that sell directly.

Read the case

Three levels, from the simplest to the most complete

  1. Basic level

    Statistical recommendations

    Customers who bought this also bought, products from the same line, rotating bestsellers. Calculated every night, instant response.

  2. Advanced level

    AI and page context

    A light AI reads category, search and active filters and combines them with the basic-level rules. If it does not answer within 8 seconds, the basic level takes over.

  3. Full level

    Model trained on your shop

    A proprietary AI model trained on your sales, retrained every month on the last 6 months. Different suggestions for new, regular, occasional and high-value customers.

AI suite for e-commerce

conversational commerce and visual search

An assistant that guides undecided shoppers, search by photo and live inventory filters, on the website and on WhatsApp.

Who it is for: Online shops with a wide catalogue.

AI agents

multi-agent systems

Agents that carry out specific tasks (sales, support, analysis) with your data, your tone of voice and your rules. They work on the website, email, WhatsApp and CRM.

Who it is for: Companies that want to grow without multiplying repetitive work.

Camera-based quality control

computer vision

A vision system trained on your products recognises defects and anomalies on the line, counts inventory and alerts you when something does not add up. It can use the cameras you already have.

Who it is for: Manufacturing, warehouses, stores.

How it works

The machine works, the person decides

Every project follows the same thread: one step at a time, with a person approving before anything really changes.

  1. 1

    Reading the data

    We analyse orders, catalogue, conversations or images: the material the system will learn from.

  2. 2

    We start from the basic level

    We put the simplest version online and measure the effect straight away.

  3. 3

    The person decides

    Review by your team

    The team checks suggestions, answers and alerts. It decides what runs automatically and what always goes through a person.

  4. 4

    We move up a level

    With results in hand, we add AI and personalisation where they bring value.

Our Human-in-the-Loop method

A real case

The engine that drives 23% of revenue

Three levels of recommendation, from simple to personalised, built on the shop's real orders.

All cases
Advanced AI & e-commerce

~23%

of revenue from recommendations, within 5 weeks

The engine that drives 23% of revenue

Fashion e-commerce · Italy

Read the case
real result

~23%

of revenue comes from the recommendation engine

with only the basic level active

Fashion e-commerce, Italy, 2025

real result

5

weeks from launch to result

statistical recommendations, no AI

Fashion e-commerce, Italy, 2025

Sartia: AI product photography for fashion: views, model, description and video from a single shot.

Related product

Sartia

From one photo to content ready to sell.

For fashion there is Sartia: from a single photo you get views, model, description and video ready for the catalogue.

Try Sartia

Frequently asked questions · Advanced AI & e-commerce

The questions we get asked most often

Which level should I start from?

Almost always the basic level: it is fast, costs less and shows right away whether recommendations work on your shop. The other levels are added when the numbers justify it.

Does the engine work with my e-commerce platform?

It slots into the website as a widget and can also reach WhatsApp. During the free analysis we check the platform you use.

Can an AI agent act on its own?

Only where you decide. The actions that matter, such as refunds, orders or sensitive replies, go through a person. Every action stays traceable.

Do I need new cameras for quality control?

Often the ones you have are enough. If more camera points are needed, we tell you beforehand, with the costs.

Who owns the model trained on my data?

The full-level model is built and trained only on your shop's data. The details on ownership and use of the data are written in the proposal.

Combines with

The other pillars

All solutions

Digital foundations

Website, backend and data in order: the base AI works on.

Explore this pillar

Automation & processes

Documents, orders and files handled by the machine, approved by a person.

Explore this pillar

Intelligence & forecasting

Mathematical models, simulations and dashboards that tell you what comes next.

Explore this pillar

Strategy & governance

Where to start, how to stay compliant with the AI Act, who leads the project.

Explore this pillar

Advanced AI & e-commerce

Have an idea in this field? Let us start there.

A free 30-minute call. You tell us where you lose time, we tell you what can be done and what it costs.

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