All use cases

Prevent / Care

Omnichannel Tier-1 resolution, scored and attributed

"Where is my order?" gets a real answer in seconds, on whichever channel the customer chose — and every resolved conversation is scored for quality and tied back to revenue.

Next best thingClose the loopBehavioral triggerChatEmailVoiceWhatsAppIn-app

What it does

Most of what lands in a support queue is Tier-1: order status, account updates, password resets, billing questions. In retail, around 70% of tickets are some version of "where is my order" — questions a live data lookup answers better than a queue ever will. The agent resolves these end-to-end across chat, email, voice, and WhatsApp, following the client's operational playbook, in the customer's own language, and escalates only when policy or confidence says a human should take over. The difference from a classic deflection bot: every resolved conversation is judge-scored for quality and attributed to a money outcome, so support leadership sees what the automation is actually worth, not just what it absorbed.

How it works

  1. 1
    Trigger. a customer writes or calls in on any connected channel — web chat, email, WhatsApp, voice, or in-app.
  2. 2
    Decision. the agent identifies the customer, pulls live order and account data, and matches the request against the operational playbook: is this a Tier-1 intent it is cleared to resolve, a regulated flow (like a disputed charge) that runs under a tighter policy class, or a case for a human?
  3. 3
    Action. it resolves in-channel and in the customer's language — a live order card with the courier's current status, an account or address update after identity verification, the first verified steps of a billing dispute — instead of a ticket number and a wait.
  4. 4
    Follow-through. anything it cannot finish escalates to a human with the full transcript and context attached; every resolved conversation is judge-scored and written back to the helpdesk with its revenue attribution (contact cost avoided, order saved), so quality and value stay measurable per conversation.
Example
AgentI found your order — it left the warehouse yesterday and the courier has delivery scheduled for Thursday. You'll get a tracking link the moment it's out for delivery.
CustomerThursday works. One more thing — I've moved, can you change my address for next time?
AgentDone. I've verified your account and updated the default delivery address. Both changes are saved to your profile, so you won't have to repeat any of this if you ever contact us again.

Configuration

How the agent is wired for this use case.

Triggeran inbound conversation on any connected channel — a customer writes or calls in via web chat, email, WhatsApp, voice, or in-app
Tools & actions
  • Helpdesk / contact-center platform · read the inbound conversation, write back the resolution, judge-score, and revenue attribution
  • OMS / commerce backend · pull live order and delivery status, surface the order card with the courier's current status
  • Identity verification · confirm the customer before any account or address change
  • Payment provider · look up billing details and run the first verified steps of a disputed-charge flow
  • Knowledge base / operational playbook · match the request against cleared Tier-1 intents and policy
AutonomyTier-1 resolution runs unattended in-channel and in the customer's language, gated by playbook-adherence checks and a judge score on every conversation; account changes require identity verification first; regulated flows (like a payment dispute) run under a tighter policy class and the agent completes only the first verified steps
Channelschat · email · voice · whatsapp · in-app
Escalationanything the agent cannot finish — or any flow policy or confidence flags for a human — escalates with the full transcript and context attached

What you need

The inputs this use case runs on. Your channels stay yours; the agent supplies the judgment.

Signals

inbound conversations on each connected channel; order, delivery, and account events for live lookups

Data

order history, account record, the operational playbook and policy documents, language preference, consent state

Guardrails

playbook adherence checks; identity verification before any account change; a tighter policy class for regulated flows like payment disputes; judge scoring of every conversation; explicit escalation rules so edge cases reach a human with context, not a dead end

Metrics it moves

  • ticket-deflectionup: Tier-1 volume is resolved without ever becoming a human ticket
  • time-to-resolutiondown: a live data lookup in seconds replaces a queue measured in hours or days
  • csatup: customers get an answer in their channel and language, first try
  • revenue-per-conversationvisible for the first time: each resolution carries its attributed value, not just a deflection count

Related use cases

Prevent / Care

Operator copilot — human-agent assist

the same machinery drafting for humans instead of resolving solo

Prevent / Care

Returns and refunds, judge-gated end-to-end

the deepest Tier-1 flow, fully automated under judge gating

Convert

Support-to-sales opportunity detection

turning a resolved support moment into revenue

See it on your own customer journey

Bring one drop-off, one churn cliff, or one silent segment. We will show you what a proactive agent with memory and judgment does with it.

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