Customer Service.
14 real jobs AI can take on for a customer service team - most of them inside the licence you already pay for. The colour on each card is the kind of AI; the line at its foot is what it takes to get it.
THE COLOUR IS THE KIND OF AI — AUTONOMY RISES LEFT TO RIGHT
The line at the foot of each card is what it takes to get it - a free chatbot, the licence you already pay for, or a build.
If these words are new, the homepage walks the whole spectrum →
Problem resolution.
High volumes of unresolved issues and reopened cases can result in decreased customer satisfaction and loyalty, affecting future revenue.
Cross-team diagnosis and a drafted, agreed reply reach the customer faster, turning a complaint into a resolved relationship.
Try it in your own AI
- Context: A customer complaint at [company] spans [teams, e.g. billing and delivery] - the history is below and the customer has already repeated themselves twice.
Objective: Reconstruct what happened across the threads, identify where it broke, and draft the reply that owns it, fixes it and gives them one named point of contact. [Paste the threads and notes.]
Style: Internal timeline first, then the customer reply under 200 words.
Tone: The reply human and unhedged - apologise once, properly, then be practical.
✓ In the licence you already pay for
Agents receive personalised cross-sell and upsell suggestions drawn from each customer's patterns, lifting revenue per interaction.
✓ In your licence - assembled once, then it runs. We set these up →
Agents get real-time, knowledge-based resolution steps tuned to the customer's mood and history, so issues close quicker.
Uses your suite's meeting AI - how this lands varies by licence.
✓ In your licence - assembled once, then it runs. We set these up →
Connected case data and team collaboration produce a fast, accurate complaint response inside the service workflow.
✓ In your licence - assembled once, then it runs. We set these up →
Case assignment.
Customer issues must be sorted and prioritized manually, which is time-consuming and can lead to slower issue resolution and a less personalized service experience.
Managers recap activity, spot performance trends and brief leadership, turning daily data into improved procedures the team adopts.
Try it in your own AI
- Context: I lead a frontline service team of [n] at [company]. Review time - and the activity data below holds real patterns I don't want to replace with anecdotes.
Objective: Find the trends: who's improving, who's overloaded, where handle times or reopens spike and what correlates. Then draft the leadership recap and the one procedure change the data supports. [Paste the activity data.]
Style: Trends with evidence, recap under a page, the procedure change as a plain recommendation.
✓ In the licence you already pay for
Support content is auto-classified, tagged and version-tracked, so agents find the right article first time.
✓ In your licence - assembled once, then it runs. We set these up →
Documents are monitored for breaches, with alerts and safer alternatives suggested before a compliance problem occurs.
✓ In your licence - assembled once, then it runs. We set these up →
At-risk interactions are flagged with remediation steps before satisfaction drops, protecting retention and reputation.
✓ In your licence - assembled once, then it runs. We set these up →
Field engineers get diagnosis, safety checks, parts lists and upsell prompts on the job, resolving faults right first time.
✓ In your licence - assembled once, then it runs. We set these up →
Work orders, technician matching and customer updates are automated end to end, cutting scheduling delays and admin.
Built for your business - the work you'd hire us for →
Day in the life.
See how people can use your AI assistant to perform common tasks throughout their day to save time, generate value, and improve their wellbeing.
A walkthrough of how a frontline agent resolves customer issues faster across a typical shift.
Try it in your own AI
- Context: I'm a frontline service agent at [company]. Between tickets I lose time re-reading long histories and writing the same explanations from scratch.
Objective: For the ticket below: summarise the history in three lines, tell me what's been tried, draft my reply in our support voice, and give me the one-line case note for the log. [Paste the ticket thread.]
Style: Four labelled parts, the reply under 150 words.
Tone: The reply warm and concrete - next step, timeframe, no scripts.
✓ In the licence you already pay for
A walkthrough of how a support leader uses AI through the day to sharpen service operations.
Try it in your own AI
- Context: I run support at [company] - [n] agents across [channels]. I get numbers all day but the shape of the week only reaches me as anecdotes.
Objective: From the ticket and satisfaction data below, give me the week's story: what drove volume, where we were slow, which category is growing, and the one thing I should raise with [product/ops] because support can't fix it. [Paste the data.]
Style: One page - four headed paragraphs, numbers inline.
✓ In the licence you already pay for
Issue diagnosis.
Service agents may lack access to documentation and subject matter experts, which can lead to inconsistent problem-solving and delay resolution.
Calls are transcribed, summarised and analysed for sentiment and trends, building a case history that speeds future fixes.
✓ In your licence - assembled once, then it runs. We set these up →
Patterns across tickets, chats and emails surface recurring issues, so teams fix the cause once and share the answer.
✓ In your licence - assembled once, then it runs. We set these up →
Most of these run on the licence you already pay for.
The gap is that nobody's pointed it at the job yet. That's the work we do, and it starts with a half-hour call - bring the one that looked most like your week and we'll tell you whether it's a quick win or a proper build. No licence yet? We'll help you pick one first.
← All twelve teams