AI for Customer Support · Solutions

Agents get a draft and the history. They keep the judgement.

A support copilot sits beside the agent. It reads the conversation as it goes, pulls up the customer’s past tickets, finds the article that applies and drafts a reply. She edits it, sends it or ignores it. Customers hear only from a person. Nothing sensitive happens without her approval.

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What it is

A chat message gives little time. In that minute she must read the thread, scroll through the customer’s old tickets, find the policy, and write something that is polite and also correct. It is a lot.

It searches and drafts first. She keeps the judgement. Every suggestion names the article or past ticket it came from, so she can check it at a glance and rewrite any part she dislikes. It drafts the notes too.

Where it goes wrong today

The problems this is meant to ease.

Agents hunt for answers mid-chat

Policy, price and fault guide live in three documents, and the agent opens each in turn while the customer waits and the chat window shows nothing.

History is skipped under pressure

Old tickets take too long to read. So the agent asks what was already answered, and the customer, irritated at repeating the same details a third time, loses patience. That costs goodwill.

Replies vary by agent

Wording and tone differ by agent. Customers compare notes. Each agent writes from memory, so nobody can say which version of the policy or the tone is the current one at any moment.

Ticket notes are thin or missing

By call ten, notes say fixed. The next agent knows nothing of what was tried, so the same checks are run again, the same result appears, and the customer waits once more.

New agents take a long time to be useful

A recruit interrupts seniors to learn. Senior agents lose their own working hours to answering the same questions, and the new hire lacks confidence on the phone.

Sensitive actions slip through

A refund is issued from memory of the policy, or a number is changed without the checks, and the slip goes unseen until the month-end review. Such mistakes cost more than slowness. They are hard to reverse. Those errors linger.

Inside the module

What AI support copilots does.

Answers found while the customer types

Help articles and policy pages appear in a side panel as the customer describes the fault, so nobody searches by hand while the chat carries on. The paragraph that applies is marked.

Reply drafts from approved material

Drafts draw on approved replies. She edits or sends it. Nothing leaves the building until she presses send.

Earlier conversations summarised

Past tickets, open orders, warranty dates and earlier promises are condensed into a few lines at the top of the screen. A third complaint about one pump shows.

Troubleshooting steps suggested

For a known fault type, the panel proposes the checks your senior agents run, in order, with the model or product noted. She may skip or add steps. The choice is recorded.

Ticket notes and call summaries

When the chat or call ends, draft notes list the problem, the steps tried and the outcome, ready for the agent to read through. She corrects them. Only then are they saved.

Approval before sensitive actions

The copilot never does refunds, account changes or anything that sends data outside the company, and each such action waits for a named approver. It prepares the action and shows why. Either she or her supervisor approves it.

Quality review of suggestions

Each outcome is logged with its reason. A lead reviews the rejected ones weekly and fixes the article or the style note that led the copilot wrong.

Help for newer agents

A new joiner sees the checklist that senior agents follow for each issue type, plus the usual wording for refusing a request politely. Your own team writes all of it.

Where it gets used

What this looks like in a working week.

A telecom-style billing desk

While a caller disputes a charge, the panel shows the bill history and the policy paragraph on adjustments, so she answers from the record. She writes the reply. The credit request goes to a supervisor.

A machinery service centre

An agent handling a stopped pump gets the model’s fault guide and the steps senior technicians use, in the order they run them. Notes fill in after the call. The service visit is booked.

An online retailer’s chat team

Agents see order status in sale week. Drafts speed up the commonest replies. Refunds still need the team lead.

A clinic’s front desk

Reception staff see appointment rules and fee notes beside each patient’s message, so replies match the clinic’s current policy and the front desk stops guessing. Anything clinical goes to a nurse. The copilot never drafts medical advice.

A software vendor’s help desk

Agents receive a summary of the customer’s earlier tickets and a draft pointing to the relevant guide, which spares the customer the opening round of repeated questions. Engineers get notes with tried steps.

Who touches it

The people who open this every day.

Chat and phone agent

Works the conversation as before, with the history and a draft already on screen, and ignores the panel whenever it does not help. Rejects suggestions that miss. A tick-box records why.

Team lead

Reviews which suggestions agents accept, edit or reject. Then fixes the article behind frequent rejections.

Trainer

Sits with new joiners at the side panel. Reads their corrected drafts to see where wording needs coaching, and which articles confuse people, then feeds both back to the article owners.

How it is set up

From first conversation to daily use.

Discovery at the agents’ desks
We watch a day of real chats and calls and note where agents stop to search, scroll or retype, which sets the list of problems worth solving first. The helpdesk, the shared drive of policies and the call system are listed as sources, along with who owns each and how often it changes.
Getting the knowledge ready
Articles are checked for age and conflicts, approved replies are collected, and closed tickets of good quality are chosen as examples. Weak areas go to your team first.
A trial on closed tickets
It runs on closed tickets only. Its drafts and summaries are laid next to what the agent actually wrote, and gaps are marked for the article owner to fix. Customers see nothing.
Checking with the agents themselves
A small group of agents uses the panel on live work for a week, with a tick-box for rejections and a short call each afternoon to compare notes. They tell us which suggestions help.
Going live, team by team
The panel opens for one team. A switch lets agents ignore it. Approval rules are set before anything else.
Reviewing what changed afterwards
The lead reads accepted, edited and rejected suggestions, fixes the articles behind the worst misses, and decides whether further teams, channels or call transcripts are worth adding.

Measuring it

How you would know it is working.

Record where you stand before it goes live, so the change is measured against your own figures.

Average handling time is the length of a contact from first message to closed note, taken from helpdesk timestamps for the same teams before go-live.
Agent productivity is the number of contacts closed per agent in a period, counted from the helpdesk history that exists before the copilot starts.
Resolution quality comes from a reviewer scoring a sample of closed tickets against your own checklist, using the same reviewer each time. Earlier scores set the baseline.
Suggestion acceptance is tallied weekly as taken, edited or rejected, and the first month sets the starting point for the lead.
Repeat contacts about one issue are counted. Earlier ticket history is the baseline.

Used responsibly

Where a person stays in charge.

Every sent word is approved

The copilot suggests and the agent decides. A draft cannot reach a customer without her pressing send, and ignoring the panel leaves the chat running exactly as before.

Sensitive actions need a named approver

Refunds and exports are only prepared. Account changes follow the same rule. Your team names the approvers for each type. Every approval is stored on the ticket.

Drafts can be wrong

Old articles give confident but poor suggestions. Gaps do the same. Each draft names its source so the agent can check it, and rejections point to the article that needs fixing.

Acceptance counts are for improving articles

Acceptance reflects the article, not the person. Do not rank staff by these counts. Customer data shown stays within existing access.

How it fits

It is one part of a system, not an island.

A module earns its place by what it passes to the next one. These are the connections we set up most often.

Helpdesk and ticket management
Knowledge base
Live chat and WhatsApp
Telephony and call transcripts
Order and warranty records

Already built in

Products that include it.

Built for you

How we would put it in.

Common questions

Questions owners ask about AI support copilots.

Can a draft reach a customer unseen?

No. It only suggests. She reads, edits and sends it herself. Ignoring the panel changes nothing.

What if the draft is wrong?

It happens, usually from an old article. Each draft names its source, so she spots the fault and rewrites it before anything is sent to the customer who is waiting for an answer. Rejections are collected and counted. The team fixes the article behind them.

What data and history does it need?

It helps to have a few hundred well-written closed tickets, a current set of articles and one person who owns them both. With less, drafts are plain. The panel is mostly a search box. That still saves some time.

What does it connect to?

Your helpdesk, knowledge base, chat or WhatsApp tool, and, if you want call summaries, your phone system’s transcripts, as well as the shared drive where policies live. Each link depends on what your software opens up. We check that before work starts.

Who approves risky actions?

People you name. It prepares a refund or account change. It shows the reasons. An agent or supervisor approves it. The ticket stores the approval for audit.

Is customer information kept private?

Only what the agent could open. Your adviser sets the retention period. Each country sets its own privacy rules. Legal advice is not something this software offers.

Will agents feel watched or replaced?

Not if it is introduced honestly. It removes searching and typing, and leaves judgement and kindness with the person, which is the part of the job customers remember. Counts exist to improve articles. They never rank people.

When is it not worth the effort?

A tiny team can share notes. Cost is quoted after discovery. Your main task is keeping articles current.

Who needs it most

Businesses that ask for this.

Next to it

Modules that work with it.

Talk to us

Tell us how you handle AI support copilots today.

A spreadsheet, a register or another system: say which, and we will tell you plainly what is worth changing.

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