Persona
E-commerce
Make AI work in e-commerce by fixing scattered customer and operations data—then ship one measurable use case.
E-commerce AI fails when data is fragmented across the stack.
Most teams have the tools—Shopify, ad platforms, email, support, WMS, analytics—but the data doesn’t reconcile. That makes AI outputs untrusted and automation fragile.
Ancient Labs helps e-commerce teams unify the core data surface, then ship one AI use case tied to a KPI.
Common pain points
- Customer identity scattered across storefront, email, ads, and support
- Attribution and performance reporting that changes depending on the source
- Operations data (inventory, fulfillment, returns) disconnected from analytics
- AI experiments that can’t be audited, measured, or maintained
What we do
- Map your stack and data flows (storefront, marketing, support, ops, finance)
- Assess readiness: quality, definitions, access, ownership, governance
- Identify 1–2 feasible use cases with measurable upside
- Optionally implement the foundation: connectors, pipelines, quality checks
What you get
- A readiness scorecard + gap analysis (clear and practical)
- A shortlist of use cases ranked by effort vs. impact
- If implementing: a clean, reliable data layer for one primary surface (e.g. customer + order)
- Basic monitoring + documentation so the foundation stays healthy
Typical first use cases
- Support automation with RAG grounded in policies + order context
- Internal search for ops teams across orders, returns, and incidents
- Smarter segmentation based on consistent customer + lifecycle signals
- KPI dashboards that reconcile marketing + revenue + operations
Ready to make AI boring (and reliable)?
Start with a Readiness Call, or request an Architecture Review to pressure-test your plan.