A household-name retailer was losing sales every peak weekend: empty shelves on promoted items, frustrated online shoppers, and service teams juggling three systems to answer one question. In 22 weeks we piloted four AI assistants—one on the website, three behind the scenes—that work within clear rules, escalate when unsure, and leave a full audit trail. The goal was simple: act on what the business already knows, faster, without putting customers or brand trust at risk.
From the outside, this retailer looked digitally mature—strong e-commerce, modern analytics, helpful copilots in every department. Inside, customers still felt the gaps. A parent planning weeknight dinners hit “out of stock” on items shown as available. A promotion drove traffic to products the warehouse could not ship in time. Seasonal peaks turned small delays into empty aisles, margin givebacks, and one-star reviews. Store and care teams were not short on data; they were short on time. Every pricing tweak, replenishment call, and service reply waited on handoffs across merchandising, supply, and care—often long after the shopper had already moved on.
We started where customers feel pain, not where tools live. With merchandising, supply, care, and data leaders we mapped four moments that matter: helping someone shop with confidence, keeping high-velocity items on shelf, protecting margin when promotions and inventory clash, and resolving tier-one issues without bouncing people between teams. For each moment we agreed on a plain outcome—assisted conversion, in-stock on key SKUs, fewer stockouts during promos, faster first response—and only then designed assistants with explicit “act,” “ask,” and “recommend” boundaries. The pilot stayed in one business unit so we could prove shoppers and teams felt the difference before scaling.
Rather than another dashboard, we connected assistants to the systems teams already use—orders, inventory, pricing, customer profiles, and case history—so answers and actions happen in the flow of work. A shared foundation handles permissions, testing against real scenarios, and logging every automated step with enough context for care, compliance, and leadership to trust what happened. Shoppers see a smarter discovery experience on the site; merchandising and supply see replenishment and promo decisions tightened; care sees drafts and routing with full order and policy context attached. Humans stay in charge where judgment matters; the assistants handle repeatable decisions inside agreed limits.
Four assistants—one customer-facing, three supporting the teams shoppers depend on—each tied to a clear outcome and clear escalation when confidence is low.
Helps visitors plan real baskets in plain language—for example, “four weeknight dinners under $80, no dairy”—using live catalog, price, delivery, and availability. Surfaces alternatives when something is unavailable and respects preferences like dietary needs, origin, or certifications so recommendations feel personal, not generic.
Watches sales and inventory signals and flags stockout risk before shelves go empty. Within agreed limits it can trigger replenishment; when risk is high or data is uncertain, it sends a recommended action to a human with the reasoning attached—so teams act on insight, not another alert email.
Keeps marketing and supply aligned during promotions. If demand is outpacing inventory, it can adjust promotional intensity or suggest fulfillment changes so ads do not promise what cannot be delivered—protecting both customer trust and margin.
Handles routine tier-one questions by pulling order and policy context, drafting a reply, and sending complex or sensitive cases to a person with everything attached. Shoppers spend less time repeating themselves; agents spend less time hunting across systems.
What made the pilot safe to expand: trust, clarity, and outcomes customers can feel.
Design around decisions that affect satisfaction and revenue—availability, price integrity, and resolution speed—not around whichever tool was bought last.
In-stock on key items, fewer broken promises on promos, faster care responses, and stronger assisted conversion—not vanity metrics on model usage.
Pricing, supply, and care share the same picture of inventory and commitments so customers never see a promotion for something that cannot ship.
Clear rules for what runs automatically, what needs approval, and what is suggestion-only—with escalation paths teams already understand.
Every automated action is logged with context, version, and approver when required—so brand, legal, and care leaders can stand behind what went out the door.
“Our customers do not care which system had the data—they care whether the shelf was full and whether care fixed it the first time. These assistants let us act on what we already know, with rules we can explain.”
We will tailor a roadmap to your constraints, compliance posture, and platform landscape.
