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E-commerce & Retail AI

Your store should feel personal, not like a static catalog.

ALTE builds e-commerce AI personalization, recommendation engines, product catalog automation and customer support automation based on your customer, product and behavior data.

E-commerce growth slows when every customer sees the same store.

Current state
×Low average order value
×High churn
×Weak repeat traffic
×Manual merchandising does not scale
×Customer data is underused
×Support teams repeat the same answers
Audit first

Personalization starts with your data.

Before building, ALTE audits the real workflow, data sources and constraints — so the solution fits how the team actually works.

Request Workflow Audit
Product catalog review
Customer behavior data
Purchase history
Segmentation logic
Customer journey mapping
Email / push logic
Support conversations
Infrastructure & Data

Safe connection to your store and customer data.

We connect to your product catalog, CRM/CDP, website/app data and define customer data access rules — so personalization runs on your real data without exposing it. We avoid unsupported revenue or security guarantees.

Discuss Infrastructure Setup
  • Customer data protection
  • Secure catalog & CRM/CDP integration
  • Website / app data access rules
  • Privacy-aware personalization
Use cases

What we can build for you.

01

Recommendation Engine

02

AI Shopping Assistant

03

Personalized Email / Push Logic

04

Catalog Enrichment Assistant

05

Customer Support AI

06

Churn Prediction

07

Repeat Purchase Triggers

Case example

Product Recommendation Engine

Example pilot result — figures illustrative until a verified client case replaces it.

Problem. Customers saw the same catalog regardless of behaviour, segment or purchase history.

Solution. AI-assisted recommendation logic based on product, customer and behavioural data.

16%
higher add-to-cart
9%
larger order value
22%
more product discovery
Process

How we build.

Discovery Call → Workflow Audit → Infrastructure & Data Review → Discovery Sprint → MVP Build + Infrastructure → Secure Launch & Scale.

1

Discovery Call

We identify the business problem, current workflow, expected outcome and potential AI opportunity.

2

Workflow Audit

We study your process, data, tools, users and constraints before defining what should be built.

3

Infrastructure & Data Review

We map data sources, access roles, integrations and deployment needs — and define personal data protection by design before any build.

4

Discovery Sprint

We turn audit findings into MVP scope, architecture, integrations, delivery logic and success metrics.

5

MVP Build + Infrastructure

We build the first working version, configure infrastructure and prepare secure deployment.

6

Secure Launch & Scale

We deploy, configure access, onboard the team, monitor feedback and prepare for wider rollout — data protected.

FAQ

Questions before we build.

Audit-first scoping, fixed scope, secure infrastructure and protected data — here is how ALTE works before a single line of production code ships.

Any questions? Reach out
What data do you need to personalize the store?

Product catalog, customer behavior, purchase history and segmentation. We audit what you already have before building.

We define access rules for your catalog, CRM/CDP and website data so personalization runs securely on your real data.

Yes. We connect to your store, CRM/CDP and channels rather than forcing a migration.

Let's build

Ready to make your team faster?

Let's map your workflow and identify where AI can improve response time, quality and measurable outcomes.