Customer support audit
We analyse your real tickets and tell you what comes in, how long replies take, which categories are missing and what could be automated without risk. All with figures from your own operation.
Before switching tools, look at what's actually happening
What goes wrong often starts before the helpdesk: tickets arriving without a category, automatic closures that flatter the resolution rate, or a team typing the same answer by hand all day. All of that shows up in the data, and it's worth seeing before deciding anything.
Two things usually show up in the first days of analysis: a share of tickets has no category, and automatic closures inflate resolution metrics. Then, with access to the full history, we group tickets by what the customer wrote, in every language you support. That becomes the basis for the categories and deadlines AgentDesk starts with if you decide to roll it out.
If the conclusion is that your current tool is fine and the problem is the process, we'll say so. Not every operation needs AI: with a few dozen tickets a day and very varied requests, tidying up categories and templates in the tool you already have usually pays off more.
What we analyse
Six areas, all calculated from your own tickets.
Real response times
First response and resolution, in working hours and in calendar time, and how long customers wait between one reply and the next.
Volume and seasonality
Which days and months intake spikes, by channel and by country. With a year of data you can see campaigns and sales periods.
Categories that match reality
We group tickets by what the customer wrote, not by the department they ended up in. That's how request types with no category come to light.
Closures and reopens
How many tickets close on their own without being solved and how many get reopened. This is where metrics mislead the most.
Team replies
What the team answers for each type of request, which replies repeat and which could become templates.
Automation candidates
Which categories have a clear procedure and data reachable through an API, and which should always stay with a person.
The data we ask for
We need
- Read-only API access to your helpdesk, or an export of tickets with their conversations
- At least three months of tickets. A year is better, to see seasonality
- The categories, departments, templates and deadlines you use today
- One or two interviews with the people who handle and supervise support
- The systems the team checks to answer: store, ERP, WMS, carriers
We don't need
- You to switch tools or change their configuration
- Admin passwords: read access is enough
- To stop operations or change how the team works while we analyse
Because we'll be handling your customers' personal data, a data processing agreement as required by Article 28 of the GDPR is signed before we access anything.
What you get
How we do it
Five steps. The analysis itself is quick; what sets the calendar is getting access to the data.
Access and extraction
We connect to your helpdesk API with read permissions, or work from an export. We pull tickets, conversations, categories and templates.
Interviews
We talk to the people who handle and supervise support: which systems they check, which cases eat most of their time and which metrics they look at every week.
Grouping tickets
We turn the subject, description and first customer message into embeddings and group tickets by similarity. The AI names each group and a person checks it against real examples.
Review with your team
We go through categories, deadlines and automation candidates with the people who know the cases. This is where we correct what the data can't explain on its own.
Delivery
Presentation meeting, report and roadmap. If you then roll out AgentDesk, the categories and templates are the starting point for its configuration.
Questions about the customer support audit
What data do you need to audit our customer support?
Read-only access to your helpdesk API or an export of tickets with their conversations, covering at least three months (a year is better). We also need the categories, templates and deadlines you use and one or two interviews with the team. You don't need to switch tools or hand over admin passwords.
How long does a customer support audit take?
The analysis is the quick part: once the data is available, the first pass comes out soon, and the full category study follows once there's access to the whole history. What drives the timeline is how long it takes to get access or an export and to schedule the interviews.
Does it work with any helpdesk, or if we only use a shared mailbox?
Yes, as long as we can read the data. With a helpdesk we use its API or an export. If you work from a shared email inbox, we read the mailbox and rebuild the threads. Some information is lost, since there are no categories or statuses, but response times and request types come out just the same.
What happens with our customers' personal data?
We access it with read permissions and with a data processing agreement signed before we start. The data is used for the analysis and the report works with aggregated figures. If you want to go deeper, we have a practical guide on AI agents and GDPR.
Do we have to roll out AgentDesk afterwards?
No. The roadmap explains what we'd do with AgentDesk, but the categories, templates and process changes work just as well in the tool you already use.
What usually comes after the audit
How long does your team really take to reply?
Tell us which tool you use and how many tickets come in each day. We'll explain what access we'd need and what you'd get.