AI Engineering for Customer Experience
Service assistants measured on your own tickets, handing over to people at the right moment, at a cost you can plan.
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Customer-experience teams bring us assistants that resolve the easy half of tickets and mishandle the rest, costs per conversation that rise with traffic, and no clear rule for when the assistant should hand a customer to a person.
How It Connects
Model choice lands in the LLM Selection Pack; cost per conversation in the Inference Cost Cut Pack; watching quality after launch in the Production AI Monitoring Harness; the assistant itself in Generative AI.
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Customer Experience AI FAQ
Which model should run our support assistant?
The one that resolves your tickets well enough at the lowest cost. We test candidate models on a sample of your real tickets, grade the answers against how your team resolved them, and price each option per conversation.
How do we know when the assistant starts getting worse?
By measuring it in production, not only at launch. We set up checks on resolution, escalation and complaint signals so a drop shows up as a number before it shows up in customer feedback.