Use case · Customer service
Hire for six weeks, train for two, and start again every year
A retailer makes 42% of its revenue between mid-November and Christmas. Support goes from 900 to 3,800 conversations a month. Every year, the same answer: four temps hired in October, trained in two weeks, gone by January — and the year after, it all starts over.
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The starting point
Temps cost twice: the wage, and the training that leaves with them
A temp hired in October is useful from mid-November, which is exactly when the peak starts. They learn the products, the lead times and the return conditions during the two busiest weeks — then they leave, and that knowledge leaves with them. The next year, you start again.
- 4× the usual volume across six weeks
- Training done during the peak, therefore done badly
- The knowledge acquired walks out in January
- Peak questions are the same every year: delivery, returns, availability
What gets deployed
Capitalise the knowledge instead of re-hiring it
- 01
The knowledge base keeps what the temps took away
Peak questions are known: delivery cut-off before Christmas, exchange conditions after the holidays, availability by reference. Written once, they serve every season — and the AI agent serves them immediately, with no training.
- 02
The proactive agent defuses before the question
On the order tracking page, a message fires: "orders placed before the 19th arrive before Christmas". A question avoided costs less than a question well handled.
- 03
Permanent staff keep what matters
Complaints, lost parcels, unhappy customers: what takes judgement stays human, and the four permanent staff finally have time for it instead of saying "your parcel leaves tomorrow" for the hundredth time.
- 04
After the season, nothing to dismantle
The setup is not sized: it absorbs. In February the agent answers the usual 900 conversations from the same base — and it is richer than it was in October.
What it moves
What the setup targets
- 4×
- volume absorbed without hiring
- 4 → 0
- temps to train each season
- 1
- knowledge base that grows year on year
- 0
- knowledge walking out in January
Go further
The building blocks used
AI chatbot
The conversational agent that answers on your site, 24/7.
DiscoverKnowledge base
Your company's knowledge, serving the chatbot and your teams.
DiscoverShared inbox
Every channel in one team inbox, wired to the CRM.
DiscoverTickets & SLAs
Requests tracked, deadlines kept, delays made visible.
DiscoverWeb pages
Publish campaign and service pages from your CRM, with no developer.
DiscoverFAQ
Frequently asked questions
Does the AI cost more during the peak?
It is billed in credits, on use: yes, it costs more for six weeks and almost nothing in February. That is exactly what you want from a seasonal cost — and the opposite of an annual package sized on the peak.
Do we still need human reinforcements?
Often one or two, not four. What changes is the nature of the work: they handle complaints, not delivery questions. The useful training is shorter, and what they learn enriches the base.
How long to prepare a season?
The knowledge base is the bulk of it, and it is written from what your team already answers. Allow a few days the first year, a few hours after that.
Does the account have to be resized before the season?
You move capacity up a tier for the peak and back down afterwards. AI credits follow usage with no annual commitment — which is precisely what a package sized on the peak cannot do.
How do we prepare for the peak without hiring too early?
The knowledge base and the scenarios are built out of season, when the team has time. At the peak, what was written runs: extra people arrive for the cases that deserve them, not for repeat questions.
Another question? Write to us — a human will answer.
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