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Arixent

Industries

Retail & E-commerce

Personalization, demand forecasting, search and customer data platforms.

Talk about your sector
Shopper checking her phone while picking fruit in a grocery store

Overview

Retail margins are decided by forecasting, pricing, personalization and operations, and each of those is now a data and AI problem. Customers expect relevant products, fast delivery and support that knows them. Retailers expect systems that keep up during peak season.

We build the customer data platforms, forecasting and recommendation engines, search experiences and commerce platforms that retailers and marketplaces run on, from the storefront to the warehouse.

Working on this in your sector? Let's compare notes.

Challenges

What gets in the way

Demand you cannot predict

Forecasts miss promotions, seasonality and new products, leading to stockouts and markdowns.

Personalization that feels generic

Recommendations ignore context, inventory and margin.

Fragmented customer data

Online, in-store, loyalty and support data never form one profile.

Search that loses sales

Customers cannot find products that are in stock.

Peak-season fragility

Platforms and integrations strain under traffic and order volume.

Support cost at scale

Order, return and delivery questions overwhelm teams.

Bring the use case. We will bring the engineering point of view.

Where it fits

Typical use cases

  • Store and SKU-level demand forecasting
  • Personalized homepages, emails and offers
  • Visual and semantic product search
  • Returns and delivery support automation
  • Loyalty and lifecycle marketing analytics
  • Marketplace seller and catalog tooling

FAQ

Questions about Retail & E-commerce

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.