Product Recommendation Engine
Example pilot result — figures illustrative until a verified client case replaces them.
A e-commerceteam came to ALTE with a workflow that did not scale. Before any build, we ran a workflow audit and an infrastructure & data review to understand the process, data sources, users and constraints.
Business problem
Customers saw the same catalog regardless of behaviour, segment or purchase history.
Audit findings
The audit mapped how work was really done, where AI could create value, which systems and data were involved, and what access roles and infrastructure the solution would need. That produced a technical brief and a fixed-scope MVP definition.
Infrastructure & data setup
We defined data flows, access roles and integration points so the solution runs securely inside the client environment, with personal data protection by design and human control points where the cost of being wrong is high.
Solution
AI-assisted recommendation logic based on product, customer and behavioural data.
Result
- 16% higher add-to-cart
- 9% larger order value
- 22% more product discovery
What to scale next
With the pilot validated, the next step is widening scope to adjacent workflows, hardening the infrastructure and rolling out to more of the team — backed by measured results rather than assumptions.
Related: E-commerce / Retail · AI services · Book a Discovery Call
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