Solution · Customer Service & Engagement

Enable AI to interpret the request, execute the action and complete it

Deflection was the measure of the last decade; resolution is the measure of this one. This solution deploys AI that does not merely answer: it retrieves the information required, acts within your systems and completes the request, on voice as well as digital channels.

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Overview

AI measured on completed requests rather than contained ones

The difference between an answer and an outcome is access to the systems where the work happens.

This solution places AI agents on voice and digital channels. The agent determines what is being requested and verifies it against your approved knowledge and the customer's actual record. It then completes the work: the system of record is updated, the case is raised and the change is confirmed to the customer. Where policy or complexity requires a person to handle the request, the agent prepares the case and hands it over with a summary rather than a transcript. Each agent is defined by X-Shift around a stated outcome, the actions it is permitted to take and the point at which it must stop, and is then deployed across the channels already in service.

The agent layer is X.24, which is built by X-Shift, so its permitted actions are defined against your integration list rather than a connector catalogue maintained by a third party. Voice is also a first-class channel, not a module added after the chat product was released.

Key features

Key features

01

Intent understanding

The customer's request, established from the way it is phrased rather than from a menu path.

and 5 more below

AI-Powered Customer ServiceIllustrative
FEATURE Intent understanding Live Type a reply… SUGGESTED Voice agents Digital agents Knowledge grounding Conversation illustrative

Customer context

The agent operates against the customer's actual record, entitlements and open cases rather than a generic script.

System actions

Permitted transactions are executed in the systems of record under named credentials, and the result is confirmed.

Handover preparation

A summarised case, including the intent, what was attempted and what remains, passed to the person assuming the interaction.

Guardrails

Topic boundaries, prohibited statements, escalation thresholds and the actions an agent must never take unattended.

Outcome measurement

Resolution rate, action success, escalation reason and containment are measured for each agent and intent.

The challenge

The bot answered. The customer still called.

Conversational automation has been widely deployed and has consistently disappointed.

The reason is structural. A bot that can answer but cannot act will always return the customer to a person at the point where the work begins. Containment rates appear healthy, yet call volume does not fall. Customers learn that the automated channel is a preliminary step before the real one, and behave accordingly.

Automation that ends at an answer rather than an outcome

Scripted decision trees that fail when the customer phrases a request differently

Answers generated from content that has not been approved

No access to the systems holding the customer's actual record

Voice excluded from the AI strategy entirely

Handovers delivered to a person as a transcript rather than a summary

How it works

Intent to resolution

Each step is a functioning capability rather than a stage in a script. The AI establishes what the customer requires, grounds itself in approved information and the customer's actual record, determines which action is permitted, executes it, and either resolves the request or hands it over fully prepared.

01

Customer intent

The request as stated by the customer, on the channel of their choice.

02

AI understanding

Intent, entities and context retained throughout the conversation.

03

Knowledge & context

Approved content, the customer record and the applicable entitlements.

04

System action

The transaction completed in the system of record, in accordance with policy.

05

Resolution

Outcome confirmed, or handed to a person with all gathered information attached.

AI service resolution path

Arabic

Saudi dialect, on voice and in chat

Most conversational AI is evaluated in English and deployed in Arabic, and the gap becomes apparent on the first call. This solution is built the other way round.

01

Dialect, not just Arabic

A customer who states a request in Saudi dialect is understood and acted upon, rather than being steered towards formal phrasing that the system can parse.

AI-Powered Customer ServiceIllustrative
FEATURE Dialect, not just Arabic Voice CONVERSATION TURNS Language Arabic and English Dialect Saudi Voice The same agent on… Handover On low confidence Settings illustrative

Coverage

Voice AI and digital AI, not one or the other

Voice is where the difficult conversations occur and where most service cost still resides. An AI strategy that covers only chat addresses the less costly half of the problem.

Voice AI

Inbound and outbound telephony

WhatsApp

Conversational messaging

Web chat

Site and portal

Email

Structured and free-form

Portal

Authenticated self-service

Mobile

In-app assistance

Messaging

SMS and social

One agent definition

Shared across all channels

Operating model

Three modes, chosen per contact

The decision is not AI versus human, but which of the two is best placed to handle this contact, given its complexity and the applicable policy.

DESIGNED BEFORE IT IS BUILT

Decisions are made in sequence, and each one is recorded.

Mode 01

AI resolves

The request is completed end to end, with the action recorded in the system of record and confirmed to the customer.

OutputA closed case with the transaction recorded and the customer notified.
Mode 02

AI assists

A person handles the contact, with AI providing context, knowledge, suggested responses and the next best action.

OutputA shorter handling time and a response that the agent did not need to research.
Mode 03

Human handles

The contact is routed to a person with the case prepared and summarised, rather than a transcript to review.

OutputA case summary, the customer's intent and what has already been attempted.

Outcomes

What changes for the operation

What this changes

When measured against resolution rather than deflection, the effect is visible in the contacts that never need to return.

01

Requests completed within the automated channel rather than transferred

02

Voice volume addressed in addition to digital volume

03

Answers grounded in approved content, with the source available

04

Human capacity released for contacts that require judgement

05

Handovers that resume from the point where the customer left off

06

Service available outside working hours without additional headcount

Use cases

Requests that currently end in a call-back

The difference in each case is that the automated handling concludes with a completed transaction rather than a promise to call back.

01

Card blocking outside working hours with X.24

Situation

A cardholder notices an unrecognised transaction at midnight, and the only available route is a queue that opens at eight, so the card remains active overnight.

What happens
  1. Understand the request
  2. Retrieve and check
  3. Act in your system
  4. Confirm the outcome
Outcome

The block is applied in the card system during the call and confirmed to the cardholder before the conversation concludes.

Commonly applied in Banking and across other sectors

02

Plan changes the chatbot cannot make with X.24

Situation

A subscriber requests a plan change in chat and is given the eligibility rules, but must then telephone because the chatbot cannot make the change.

What happens
  1. Understand the request
  2. Retrieve and check
  3. Act in your system
  4. Confirm the outcome
Outcome

Eligibility is verified against the actual account and the plan change is written back, so no second contact is created.

Commonly applied in Telecommunications and across other sectors

03

Arabic voice enquiries about application status with X.24

Situation

Most status enquiries are received by voice in dialect, and the menu-driven line cannot provide a beneficiary with anything beyond office hours and a reference number.

What happens
  1. Understand the request
  2. Retrieve and check
  3. Act in your system
  4. Confirm the outcome
Outcome

X.24 retrieves the application record during the call and informs the beneficiary of the current stage and any outstanding requirements.

Commonly applied in Government and across other sectors

More ways to apply it

Billing queries at month end with X.24

Meter readings are submitted and bills explained in the automated channel, leaving the queue for faults that require a person.

Utilities

Returns during a promotional peak with X.24

Returns are raised and status is confirmed automatically, so the peak is absorbed without recruiting for a fortnight of demand.

Retail

Client programmes with different handling rules with X.24

Routine intents are handled automatically for each programme, and staff are assigned to contacts where client rules require judgement.

BPO
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These are illustrative applications, not client results. The intents to be automated first depend on your contact reasons and on what an agent is permitted to write back.

Why we build this

Why the agent layer is ours

Voice was not added later

Most conversational platforms were built for chat and acquired telephony afterwards, which is evident in interruption handling and latency. X.24 was designed with voice as a first-class channel, because in this region voice remains where the most demanding service interactions occur.

Arabic is a language, not a locale setting

Dialect, code-switching between Arabic and English mid-sentence, and Arabic names in transcription are handling challenges rather than translation challenges. Owning the agent layer enables us to tune them against your own interactions.

The action layer operates across your estate

An agent that cannot write to your systems is a chatbot. Because the platform is built by X-Shift, permitted actions are defined against your integration list and credentials rather than a published connector catalogue.

Where a partner platform meets the requirement better, that is what we will propose.

Before you evaluate

What to establish first

Common questions

What buyers ask before committing

Next

Where to go next

Put one real intent through it

Select a high-volume request that currently ends in a call-back, and observe an agent take it end to end against a sandbox of your systems, including the handover when a boundary is reached.

Applicable where demonstrations have been shown and one is required against your own transaction.

Request a working demonstration

Decide where AI belongs first

Contact Centre Transformation establishes which intents should be automated, which should remain with people, and what the operation looks like when a third of the volume no longer reaches a queue.

Applicable where the automation target was set before the contact reasons were analysed.

Contact Center Transformation

Organisations that automate before deciding what should be automated usually rebuild within two years; where we judge this to be the risk, we will state so.

Where this sits

Related

Runs onX.24The agentic AI platform behind this solution.
Designed byContact Center TransformationAI strategy inside the wider operation.
Deployed asKnowledge & Self-ServiceThe approved content AI answers from.
Deployed asAgentic AI & Enterprise AutomationThe same capability beyond customer service.

Also relevantOmnichannel Customer ServiceCX Technology Advisory & Integration

Technology that enables it

What this is built on

These are platforms built and supported by X-Shift. Where your estate already covers one of them, the engagement becomes integration and operating model work rather than a platform deployment, and we will state this clearly.

Enterprise Agentic AI Platform

AI agents that establish intent and complete the transaction within your systems.

Explore
Customer appointment booking journeyIllustrative
X X.24 Agent ● Online · typically replies instantly Today I want to move my appointment to next week 09:41 Of course. Your appointment is on Sunday at 10:00. Tuesday at 09:30 and Wednesday at 14:00 are available. 09:41 ✓✓ Tuesday please 09:42 Done. Your appointment is confirmed for Tuesday at 09:30. The details have been sent to you, and a reminder will follow the day before. 09:42 ✓✓ Appointment updated · REF 48192 Reply… AGENT REASONING 2.4s Intent understood Reschedule an existing appointment Customer identified Record matched on the calling number Knowledge applied Rescheduling window and notice policy System action Availability read, slot written back Outcome completed Confirmation and reminder issued CONNECTED SYSTEM Appointment updated · REF 48192 Booking engine · synced 2.4s ago ESCALATION RULE Clinical questions go to a person Routed to the on-duty nurse within 30s Sample data. Handled end to end - no queue, no callback.

X.24 - illustrative interface, not a product screenshot.

Is this the capability you require?

Describe what is not working today, and we will advise whether this is the right answer, a smaller piece of work, or an entirely different problem.

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