Industry

AI for E-commerce

Search, recommendations, catalogue quality and support automation, measured on conversion, not on accuracy.

Capabilities
69
Regulations in scope
4
Systems integrated
5

The constraint that defines this sector

In e-commerce, every change must be justified by a controlled experiment against revenue. Everything we build here is shaped by that before it is shaped by the technology, the guardrails, the approval points and the evidence trail are design inputs, not things added before go-live.

Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. We hand over with runbooks, tests and a team that knows how it works, not a dependency.

Sector context

Defining constraint
every change must be justified by a controlled experiment against revenue
Regulations in scope
consumer protection e-commerce rules · DPDP Act 2023 · GST · return and refund policy requirements
Systems of record
Shopify, Magento or custom storefronts · OMS · payment gateways · logistics aggregators · CRM
Where we usually start
catalogue enrichment and attribute extraction

Workloads worth automating here

  • catalogue enrichment and attribute extraction
  • search relevance
  • product recommendations
  • return-reason analysis
  • support automation

Capabilities for e-commerce

Questions from this sector

How quickly can we see conversion impact?

Search and recommendation changes usually show within two to four weeks of experiment traffic, assuming enough volume to reach significance.

Can you fix our catalogue data?

Yes, attribute extraction from images and descriptions, plus deduplication. Catalogue quality quietly limits both search and recommendations.

AI in e-commerce, where would you start?

Bring us the constraint, not the technology. We will tell you what is realistic under it.

Or email bd@dtrasglobal.com · call +91 74118 77878