customer_support · ecommerce · workflow
Wayfair's Wilma LLM copilot helps customer service agents resolve issues 12% faster
Customer service agents had to navigate complex chat interactions while applying hundreds of Wayfair policies and maintaining empathy, a task that became especially challenging at peak shopping volume.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Agent selects action type
The agent clicks a button to select what they want Wilma's help with (Discovery, Resolution, Empathy, or Give Me a Minute).
Tools used
GeminiGPTJinja
Outcome
Agents using Wilma address customers' needs 12% faster and adhere to Wayfair's customer policies between 2% and 5% more, depending on issue type.
What failed first
The first version of Wilma used a single long prompt triggered by one button; the LLM frequently followed irrelevant examples, agents felt they lacked control, and during resolution negotiation the model sometimes offered inappropriate resolutions.
Results
Volume12% faster
Running since2023
Grounding & classification
Source type: technical build writeup
23 fields verified against source quotes.
agent assistcontent generationmulti agent workflowchat transcriptfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedecommerceaccuracy improvementcycle time reductionemployee productivitytechnical build writeupcustomer supportai draft human approval