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Solution · Customer serviceEvery customer question lands in one queue, and none goes unanswered
One inbox instead of four channels
A robot gathers messages from email, Messenger, the website form and phone notes into one queue. AI recognises the topic, attaches the customer’s history and proposes a reply from your base; a human approves with one click. Every case has a status, and response time stops being a channel lottery.
Executive summary
Customers write wherever suits them: email, Messenger, the form, the phone; every channel has a different keeper and different customs, so replies come out inconsistent, doubled or not at all.
All channels flow into one queue; AI recognises the topic, attaches the customer’s history and proposes a reply from the company base; the team approves, corrects or writes their own; statuses make sure nothing hangs.
Response time falls and evens out across channels, doubled and contradictory replies disappear, and the owner sees for the first time what customers ask most and where service loses time.
mailboxes and social channels; one queue in Microsoft Teams; the company answer base; a weekly report
Business problem
Four channels, four truths, one reputation
A small firm does not choose its contact channels; its customers do. One writes an email, another a Messenger message, a third fills in the website form, a fourth calls and asks to be called back. Each channel appeared at a different moment, has a different keeper and a different checking rhythm, and together they form a system in which nobody sees the whole.
The consequences are predictable. The Messenger message has hung since Friday, because the social media person is off. Nobody has looked at the form for a week, because it lands in a mailbox nobody uses. A customer who wrote in two channels gets two different answers, one of them outdated. And the questions repeat in circles: opening hours, prices, dates, returns, the same ten topics written anew every day, differently by every hand.
Response time is a channel lottery: email is lucky to get an hour, Messenger three days. The customer does not understand that and does not have to; he judges the firm by his worst experience and writes about it where everyone can see. Meanwhile the team, instead of serving, juggles tabs and memory: who already replied, what was promised, in which channel.
Most interesting of all, the knowledge in those conversations is lost entirely: nobody knows what customers ask most, which questions end in sales and which in frustration. That knowledge is a ready map for improving the offer and the website; it lies in four mailboxes, unread.
How it works today
Below is what the work looks like before anything is automated.
- PersonEvery channel has its own keeper and checking rhythm; nobody sees the whole
- Risk of errorA message in an unkept channel hangs for days or vanishes for good
- Risk of errorA customer writing in two channels receives two different answers
- PersonThe same ten questions are written anew daily, differently by every hand
- WaitingResponse time is a channel lottery; the customer judges the firm by the worst
- Risk of errorThe conversations’ knowledge is lost; nobody knows what customers ask most
Why the current process costs more than it appears
The bill that never shows up in a budget.
- An unanswered message is the cheapest possible way to lose a customer who came by himself.
- Contradictory replies from two channels erode trust more effectively than no reply.
- Writing the same answers anew is hours a week spent on retyping.
- A team juggling four tabs serves slower and tires faster.
Cost of inaction
The first row cautiously assumes only part of the unanswered messages were genuinely lost sales; even that fraction carries the whole argument. You will see your own numbers in the first report, because the queue measures everything from day one.
The model does not price reputation: the opinion “they don’t reply” outlasts many an advertising campaign and costs more.
A model organisation with realistic proportions – the numbers exist so you can run the same maths on your own data; they are not a client result.
A services and retail firm with customer service across four channels: email, Messenger and Instagram, a website form, phone with call-back requests; around 650 messages a month, three people replying alongside other duties, Microsoft 365.
Every channel checked separately, on its keeper’s rhythm; the cherry on top: the form lands in an unused mailbox.
Response times from an hour to three days, channel depending; every eighth message unanswered.
Repeatable questions written anew; no knowledge of topics or volumes.
All channels flow into one Teams queue; AI recognises the topic and the customer, attaches the history of previous conversations and proposes a reply from the company base; the team approves with a click or corrects; a case without reaction escalates after a set time; the weekly report shows topics, volumes and times.
In the modelled case the median response time falls below an hour within working hours, unanswered messages vanish, and the team handles the same traffic in half the time. Model numbers, not the firm’s records.
Proposed solution
We start with the answer base: together we write down the twenty or thirty topics that keep returning, and model answers in your tone, with variants where needed. That is the system’s heart and your property: the AI proposes from the base, not from its own fancy, and the base grows with every new topic you approve.
We tie the channels into one Teams queue: email directly, Messenger and Instagram through their interfaces, the website form, phone notes through a simple reception form. Every message gets an AI-recognised topic, the customer’s history from all channels attached, and a proposed reply. A human approves with one click, corrects or writes their own; nothing goes out without a human decision, unless you yourselves designate fully automatic topics, like opening hours.
Statuses watch the whole: a case without reaction for the set time escalates to the owner, and a customer writing in a second channel joins the same case instead of opening a parallel one. Once a week the report arrives: topics, volumes, times per channel, questions with no good answer in the base. It is the first true map of what your customers want from you.
UiPath Orchestrator: the message queue, time-based escalations, retries and an audit trail; UiPath Integration Service connectors for Microsoft Teams and Outlook 365; AI models for classification and reply proposals
One queue from all channels, cross-channel customer history, proposals from your answer base, statuses with escalations and the topics, volumes and times report
Email via Outlook 365; Messenger and Instagram via their interfaces; the website form; phone notes via a form; the answer base in SharePoint
How the automated process works
- AutomationMessages from all channels drop into one Teams queue
- AutomationAI recognises the topic, attaches the customer’s history and proposes a reply from the base
- PersonThe team approves with a click, corrects or writes their own; the decision is human
- SystemTopics designated automatic go out by themselves: hours, prices, directions
- AutomationA case without reaction for the set time escalates to the owner
- PersonThe weekly report: topics, volumes, times per channel, gaps in the answer base
Human-in-the-loop model
Automation handles
- Gathering from all channels, classification and proposals from the base
- Statuses, escalations and joining one customer’s cases across channels
- The weekly topics, volumes and times report
People decide
- Approving replies, and every atypical, sensitive and complaint case
- The answer base: its tone, content and the decisions on what may go automatically
- The conversations that should be conversations: a call to the customer instead of a tenth message
Before and after
Systems and integrations
The stack is short on purpose: one engine, one execution layer, one place where a person decides.
Inputs
- emails, social messages, the form, phone notes
- the company answer base
- escalation rules and automatic topics
- the customers’ case history
Automation layer
- UiPath Orchestrator
- UiPath Robots
- UiPath Integration Service
- UiPath Action Center
Target systems
- one case queue with statuses
- sent replies with history
- the topics, volumes and times report in Microsoft Teams
Human touchpoints: the queue in Microsoft Teams; escalations to the owner; the weekly report; an answer base review quarterly
Technologies used
the message queue, time escalations, retries, a record of every reply
Atopic recognition and proposals from your base, in your tone
Athe queue, approvals, reports
Athe answer base and the case register with history
AMessenger and Instagram via their interfaces; scope depends on the platforms
BIllustrative economic model
Numbers you can check against your own data.
The model assumes some of the lost customers were retainable by a plain, fast reply; it assumes no conversion miracles. Volumes and values belong to the scenario; your own numbers appear in the first weekly report.
Run the maths on your data
An illustrative estimate based on your inputs. It models freed capacity, not promised savings.
Business benefits
- No message vanishes; each has a case, a status and a deadline
- Response time evens out across channels and falls below an hour
- Repeatable questions handle themselves with a proposal and a click
- The customer gets one consistent answer, whatever the channel
- The owner sees what customers ask and where service loses time
The management view
- Customer service stops being a sum of private mailboxes; it becomes a process with numbers
- The answer base is capital: a new hire serves like a veteran from the first week
- A new contact channel is a new source in the queue, not a new tab and new chaos
Board-level KPIs
median response time per channel · unanswered messages · replies from the base versus written anew · escalated cases · the week’s most frequent topics
Security and governance
The automation has exactly the permissions it needs. Not one more.
- The robot works on customer conversation content; queue access by role
- The AI proposes solely from your base; sensitive content always goes to a human
- Every reply has an author: a human, or a topic you designated automatic
- Case history has a set retention; customer data at a minimum
- Data stays in your Microsoft 365 tenant; the robots run in the EU region of UiPath Automation Cloud
Why now
Channels multiply and customers write wherever suits them; a firm without one queue plays roulette with its own reputation.
Customers are used to replies within the hour; three days of Messenger silence reads as “they don’t want me”.
AI has learned to propose replies in a firm’s tone well enough that approval takes seconds; that lever did not exist two years ago.
Relevant executive roles
Sleeps calmly: knows no question hangs, and sees the service in numbers
One queue instead of four tabs; proposals instead of writing anew
Gets a fast, consistent reply, whichever channel he wrote through
Common questions and objections
Which is why, by default, the AI sends nothing: it proposes, a human approves. Proposals come from your base, not from the internet, and sensitive topics are flagged and always go to a human without a proposal. Fully automatic sending applies only to what you designate yourselves, usually opening hours and directions.
And exactly that remains: your team replies, in your tone, only faster, because the base proposal shortens writing to an approval or a correction. The personal character of service does not die from a tool; it dies from three days of Messenger silence.
True, which is why we tie the social channels through their official interfaces, with an alert when something changes. In the worst case a channel returns briefly to manual handling, but the queue, the base and the statuses keep working; the system does not stand on one integration.
When this is not the right solution
- A dozen messages a week on one channel: checking discipline suffices
- Service requiring expert knowledge every time, with no repeatable topics: a base will not help much
- Expecting a full chatbot without a human: this process strengthens the team, it does not replace it
A question for the next management meeting
How many customer messages hang unanswered right now across all our channels together, and how do we know?
Implementation approach
Scope without ambiguity, before anything is signed.
We deliver
- The company answer base written in your tone
- One queue from all channels with customer history
- AI reply proposals with one-click approval
- Statuses, escalations and automatic topics per your decisions
- The weekly report and two weeks of parallel running
We need from you
- Access to the channels: mailboxes, Messenger and Instagram, the form
- Two hours to write down the base of the most frequent answers
- Decisions on escalation rules and automatic topics
Stages
Discovery
Channels, volumes, response times, the most frequent topics
The base
Model answers written in your tone; sensitive topics flagged
Build
Channels tied in, the queue, proposals, statuses, the report
Parallel run
Two weeks: the queue works, the old tabs stay open; times compared
Go-live
The channels move to the queue; a base review after a month
A departmental project: it covers the whole of customer service, so the largest piece is writing the base and a week of the team’s new habits; the technology runs on the Microsoft 365 you already have.
Monday: a customer asks on Messenger about a date. Tuesday: the same by email. Wednesday: a one-star review, “they don’t reply”. All three messages will be found on Thursday.
Collect one week’s volumes from all channels and send them to us with your ten most frequent questions. We return an answer base design and the arithmetic of the time your team gets back.
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