Industry

Retail & consumer

Handle customer contact at peak without proportional headcount, and forecast demand at the granularity buying decisions are actually made.

Industry challenge
Customer contact volume is spiky and concentrated in a few repetitive request types — order status, returns, availability. Demand forecasting drives inventory cost but is often done at too coarse a grain. Product content has to be produced and maintained at scale, in multiple languages.
AI opportunity
Retail contact patterns are exceptionally well suited to agents: high volume, narrow intent range, and clear success criteria. Product content generation is a natural fit for generative models with human review, and forecasting benefits from data retailers already collect.
Solution
Customer service agents with real order and inventory access; multilingual support across channels; demand and replenishment forecasting delivered into planning workflows; product content generation with review; and automation of supplier documentation.
Business outcome
Peak contact absorbed without proportional staffing, better-informed buying decisions, and product content maintained at scale.

Where this shows up in practice

Customer service agents

Order status, returns, exchanges and availability handled end to end across channels.

Multilingual support

Service in the customer's language across chat, voice and messaging.

Demand and replenishment forecasting

Forecasting at SKU and location granularity, fed into planning systems.

Product content generation

Descriptions, attributes and translations generated at scale with human review.

Supplier document automation

Purchase orders, invoices and compliance documents processed and validated automatically.

Store and shelf monitoring

Availability and planogram compliance from existing in-store camera infrastructure.

What we design around

Bring us the specific process or constraint. We'll tell you plainly whether AI is the right answer for it.