Customer Support AI Agent Consulting

It reads the ticket or chat, retrieves the relevant policy, answers with a citation, and — where you have allowed it — takes an action on the order or account. Anything low-confidence, identity-sensitive, or outside the allowed intent set is handed to a person with the trace attached.

Either, or both. Most teams start in the helpdesk on existing tickets because the golden set is sitting there and agents can correct it. A public chat widget is a channel, not a different product.

No, not if you want quality to hold. The usual shape is the AI taking repetitive, well-bounded volume and escalating the rest. Teams that try to empty the queue in month one usually staff it again in month two after CSAT moves the wrong way.

You pick intents with a clear policy and a clear success metric — order status, password reset, return eligibility. Identity, legal threats, medical, and money beyond a documented credit go to a person. Confidence thresholds and a missed-escalation target are part of the eval suite.

Four weeks is the standard: discovery in week one, environments in week two, shadow mode on live tickets in week three, handover in week four. Going live on a bounded intent set is a config change after the sample holds.

Yes. Zendesk, Salesforce, and similar are the usual pattern — the agent reads and writes through the helpdesk’s own API under credentials you issue. If your helpdesk is not listed, an API is enough; that work is part of the implementation.

That engagement is commerce-stack specific (orders, catalog, peak season). This one is the cross-industry support agent: any helpdesk, grounded policy, scoped actions, and quality metrics that travel. Retailers with deep Shopify needs should use the e-commerce page.

You do. Prompts, retrieval index, eval suite, and runbooks are yours. Work ran in your cloud. You pay the model provider directly; we add no token markup. Thirty days of on-call is included.