Retail

AI agents for retail: quality, maintenance and supply chain

We build AI agents that help retail teams turn scattered sales, inventory, and customer data into decisions, from demand forecasting to personalized merchandising, without needing a data science team on standby.

Discuss a use case in Retail See production examples

Where our agents fit in retail

Where agents earn their place in retail

From forecasting through the storefront, our agents handle the analysis work that used to require a dedicated team.

Merchandising

Demand forecasting

Agents combine historical sales, seasonality, and promotional calendars to generate SKU level forecasts that merchandising teams can act on directly.

Customer Experience

Personalized recommendations

Purchase history and browsing behavior are turned into recommendation logic that updates as customer behavior shifts, not a one time model.

Supply Chain

Inventory reconciliation

Agents cross check inventory counts across warehouses, stores, and online channels, catching mismatches before they turn into fulfillment problems.

The stack behind it

Which pillars typically apply here

We build forecasting and recommendation pipelines using time series models and retrieval augmented agents, connected directly to POS, inventory, and CRM systems so the output reaches the teams that need it.

Explore all five pillars

Other sectors

AI agents for retail: quality, maintenance and supply chain

Where agents earn their place in retail

Demand forecasting

Personalized recommendations

Inventory reconciliation

Which pillars typically apply here

Where else we work

Governance, Risk and Compliance (GRC)

Energy

Media

Pharma

Food & Beverage

Financial Services

Healthcare and Life Sciences

CPG and Retail

Manufacturing