RETAIL & E-COMMERCE

AI for retail and e-commerce

Q: What conversion impact is realistic?

It is measured on your traffic, through controlled testing on a share of visitors. We promise no percentage before seeing your data: commerce is proven, not declared.

Q: How much historical data is required?

It depends on the use case, data quality, access and associated consent. Scoping qualifies an initial scope without assuming the history is sufficient.

Q: Can you integrate with our commerce platform?

Commerce platforms often expose APIs or integration mechanisms. We first qualify interfaces, the affected journey and security rules before choosing an integration method.

Q: Does personalization still work without third-party cookies?

It can rely on first-party data and appropriate consent, subject to the relevant purposes and rules. The setup should be designed with your product, data and compliance teams.

Commerce can combine software, data, cloud and AI to make a catalogue easier to use, prepare customer service, explore demand or compare planning options. Commercial rules remain with your teams.

Discuss your field

A specific field

Make the catalogue more useful

Browsing, history and product signals can inform a search, recommendation or discovery interface to evaluate with your teams.

Recommendations follow the session

Explore demand

Sales, catalogue, stores and seasonality can feed scenarios that help buying and supply teams prepare trade-offs.

Demand seen before the peak

Prepare pricing trade-offs

Elasticity, competition, stock levels and commercial rules can be compared without removing decisions from pricing owners.

Prices arbitrated, rules kept

It answers during the purchase

Product questions, returns, order tracking: the assistant handles the routine and hands over when needed.

Instant answers, human relay

It sees the products

Search by image, similar products, a catalog browsed differently. Vision opens aisles the search bar ignored.

The catalog turns visual

What the field requires

Seasonality and load peaks

Sales periods, holidays and commercial operations define capacity, testing and observability requirements for the architecture.

Omnichannel

Web, mobile, store: the customer is the same everywhere. Recommendations stay consistent from one channel to the next.

Latency

Expected latency is defined for the relevant journey, then guides architecture, data and caching choices.

GDPR and the end of third-party cookies

Personalization rests on your first-party data and on consent. It holds without third-party cookies.

A method that stays grounded

Work starts from an explicit journey, dataset and commercial rule. Outcomes and extension conditions are defined with teams before broader use.

01
Perceive · Continuously
Customer signals come in
Browsing, transactions, catalogue and inventory are mapped with the interfaces and performance constraints to respect.
02
Detect · In real time
Behavior becomes readable
Purchase intent, products taking off, references going dormant: the flow becomes readable.
03
Anticipate · Before the peak
Demand projects itself
By product, by store, by period. Buying and supply teams decide with time in hand.
04
Decide · As sales flow
The offer adjusts
Recommendations, prices and replenishment can be evaluated through test, validation and recovery rules defined with business owners.

What structures the work

01Data and journey map
02Prototype or service according to scope
03Testing and recovery rules
04Documented interfaces and integrations
05Team documentation

Field questions

01What conversion impact is realistic?

02How much historical data is required?

03Can you integrate with our commerce platform?

04Does personalization still work without third-party cookies?

NeuroVista, Paris

Start from a real commercial journey

Tell us about a journey, accessible data, commercial rules and the role your teams need to retain in the decision.

Get in touch